CERTIFICATE. University, Mullana (Amabala) for the award of the degree of Doctor of Philosophy in

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "CERTIFICATE. University, Mullana (Amabala) for the award of the degree of Doctor of Philosophy in"

Transcription

1 CERTIFICATE This is to certify that the thesis titled Data Mining in Retailing in India : A Model Based Approach submitted by Ruchi Mittal to Maharishi Markandeshwar University, Mullana (Amabala) for the award of the degree of Doctor of Philosophy in Computer Science, is a bonafide record of original work done under my supervision and guidance. The work contained in this thesis has not been submitted to any other University or Institute for the award of any other degree or diploma. Dr. NAVEETA MEHTA Associate Professor M.M. Institute of Computer Technology & Business Management M.M. University, Mullana (Ambala) Haryana (India) i

2 ACKNOWLEDGEMENT I express gratitude to my supervisor, Dr. Naveeta Mehta, Associate Professor, Maharishi Markandeshwar Institute of Computer Technology & Business Management, M.M. University, Mullana, whose untiring guidance had made it possible for me to complete this work. Her dedication to academic life, discipline, and straight forward approach has had a great impact on my professional and personal life. She is humane with willingness to help others, care for everyone, and always being concerned about the progress. With these rare qualities, I found in her not merely supervisor but a noble soul, a Guru. I express my gratitude to Dr. Dimple Juneja, Principal and Professor, Maharishi Markandeshwar Institute of Computer Technology & Business Management, M.M. University, Mullana, for her mentorship and guidance at various stages of this research. I am indebted to all my colleagues at MAIMT, Jagadhri, especially my Director, Dr. Raj Kumar for the constant support and cooperation throughout my thesis. I would also like to thank, though it is difficult to put it into words, my gratitude to Dr. Anil Kapil, Professor and Head, Computer Science and Technology, Haryana Institute of Engineering & Technology, Kaithal and Dr. Sangeeta Gupta, Director & Professor, Om Institute of Technology and Management (Mgt), Juglan, Hisar, who have always been there to extend all moral support and professional mentoring. iii

3 I also like to thank my mother, Smt. Raj Aggarwal, who more than a mother has always been a friend for me and has always been with me through thick and thin. I am also thankful to my better half, Dr Amit Mittal for his personal and academic support and my child, Yash to whom, I dedicate this thesis. I am especially thankful to my brother and Prime Minister awardee, Ishan Aggarwal, who, due to his academic achievements has raised the bar of academic excellence in the family. I am also thankful to my father, Shri Ishwar Aggarwal, my father-in-law, Shri Sat Paul Mittal and mother-in-law, Smt. Trishla Mittal, for showing faith in me and for ensuring a conducive environment for my professional pursuits. I offer my regards to all those who I am not mentioning, but supported me or inspired me in any respect during the course of the completion of my work. Last but not the least; I thank almighty GOD for always being there and for seeing me through the tough times. RUCHI MITTAL If I have seen further, it is by standing on the shoulders of giants --- Isaac Newton iv

4 ABSTRACT Data mining is an inter-disciplinary emerging field that focuses on access of information useful for high-level decisions and includes Machine Learning, Statistics and Probabilities, On Line Analytical processing, Data visualization, Information science, High-performance computing, etc. Data mining enables business executives to manage their data and to make relevant decisions. Simply stated, data mining refers to extracting or mining of knowledge from large amount of data. Retail is amongst the major fields of application of data mining technology. It is India s largest industry accounting for over 10 per cent of the GDP and 8 per cent of employment. In India, the industry is facing the new millennium, and the models of the past are not sufficient to ensure tomorrow s successes. Firms are increasingly relying on data mining techniques which use existing databases to devise new strategies for growth, profitability and customer loyalty. The thesis starts with the discussions on the concepts of database management systems, data warehousing and then data mining. It provides the historic development of data mining and retailing in India. This also provides the background material for the research problem. The objectives, scope and significance of the study have also been clearly outlined. Then Review of Literature provides the theoretical and conceptual framework of the research. This thesis reviews the work done in the field of interest identified since The period for this research is purposively selected so as to ensure that the technology under review i.e. Data Mining; has had sufficient time to prove its usefulness in prediction and in ensuring its use brings positive results to organizations. The major v

5 concepts and technologies reviewed are: Data Mining and Business Intelligence; Customer Segmentation and Profiling; Store Image/ Attributes; Predictive modeling through Data Mining; Cluster analysis; Factor analysis; Multiple regression Analysis. This section also identifies all the important variables and seeks to identify the gaps in the research done in the field both in India and abroad. The next section discusses the various data mining concepts, functionalities, tools and techniques. The disciplines of statistics and data mining have also been discussed to prove that these areas are highly interrelated and share a symbiotic relationship. This section helps to gain a major understanding of the various data mining algorithms and the way these can be utilized in various business applications and the way these algorithms can be used in the descriptive and predictive data mining modeling. Then the research design in terms of the type of research, the sampling plan, and the designing of the survey instrument (questionnaire) have been discussed. This section also gives the detailed description on the various data mining techniques that have been used to achieve the research objectives. The next section relates to the data analysis of the data collected through the survey and the interpretations are mentioned so that meaningful recommendations and conclusions can be drawn. The analysis was performed using the various data mining techniques like: (1) Two-step cluster analysis this technique is used for identifying clusters of customers based on their homogeneous groupings drawn from an, otherwise, set of heterogeneous customer data base (2) Chi-Square test- this is intended to test how likely it is that an observed distribution is due to chance. It is also called a "goodness of fit" statistic (3) vi

6 Factor analysis this technique is used for data preprocessing and for reducing the data to a manageable level which can be used for further analysis such as modeling and suitable interpretation; and (4) Multiple regression analysis- this predictive data mining modeling technique is used to predict the dependent variable (in this case Store Loyalty ) on the basis of the independent variables (in this case Store image dimensions/ attributes ). Finally, in the end, this thesis provides the findings, recommendations and future scope of the study. The customer groups identified are store-loyals and store non-loyals. The nonloyals present a significantly large group and retailers need to understand the typical profile of such customers so that suitable strategies can be formulated targeting them. The importance of various customer variables has also been identified. The six salient store attributes dimensions that have emerged have been discussed and suggestions have been put forth for the benefit of retailers and for future research. vii

7 LIST OF ABREVIATIONS AI ANOVA BI CART CHAID CRIS CRM DBMS DM EDI EIS FDI GRDI IR KDD MANOVA MHI OLAP Artificial Intelligence Analysis of Variance Business Intelligence Classification and Regression Tree Chi-Square Automatic Interaction Detection Consumer Image of Retails Stores Customer Relationship Management Database Management System Data Mining Electronic Data Interchange Executive Information System Foreign Direct Investment Global Retail Development Index Information Retrieval Knowledge Discovery in Databases Multivariate Analysis of Variance Monthly Household Income Online Analytical Processing viii

8 PCA PLs RDBMS RFID RFM RIS SPSS SQL VAT VSM Principal Component Analysis Private Labels Relational Database Management System Radio Frequency Identification Device Recency, Frequency, Monetary Retail Information System Statistical Package for Social Science Structured Query Language Value Added Tax Vector Space Model ix

9 LIST OF FIGURES Figure No. Figure Description Page No. Figure 3.1 Steps of Knowledge Discovery in Databases 45 Figure 3.2 Phases of Data Mining Life Cycle 47 Figure 3.3 Predictive Modeling through Linear Regression 56 Figure 3.4 Nearest Neighbors for Three Unclassified Records 59 Figure 3.5 Discovering Clusters and Descriptions in a Database 60 Figure 3.6 Hierarchical clustering 61 Figure 3.7 Decision Tree for Cellular Telephone Industry 63 Figure 3.8 Structure of a Neural network 65 Figure 3.9 A Simplified View of Neural Network 65 Figure 3.10 Neural Network for Prediction of Loyalty 66 Figure 4.1 Steps in Factor Analysis 86 Figure 4.2 Steps for Multiple Regression Analysis 92 Figure 5.1 Graphical Representation of Cluster Distribution 101 Figure 5.2 Within Cluster Percentage of Gender 104 Figure 5.3 Chi- Square - Gender 104 Figure 5.4 Within Cluster Percentage of Age 105 Figure 5.5 Chi- Square - Age 105 x

10 Figure 5.6 Within Cluster Percentage of Occupation 106 Figure 5.7 Chi- Square - Occupation 107 Figure 5.8 Within Cluster Percentage of Education 108 Figure 5.9 Chi- Square - Education 108 Figure 5.10 Within Cluster Percentage of MHI 109 Figure 5.11 Chi- Square - Income 110 Figure 5.12 Within Cluster Percentage of Shop-with 111 Figure 5.13 Chi- Square Shop-with 111 Figure 5.14 Within Cluster Percentage of Spend 112 Figure 5.15 Chi- Square - Spend 112 Figure 5.16 Within Cluster Percentage of Trips 113 Figure 5.17 Chi- Square - Trips 114 Figure 5.18 Relative Importance of Demographic and Behavioral Variables 114 Figure 5.19 Scree Plot 128 Figure 5.20 Component Plot in Rotated Space 132 Figure 5.21 Figurative Description of Store Loyalty- Predictive Model 141 xi

11 LIST OF TABLES Table No. Table Detail Page No. Table 1.1 Steps in the Evolution of Data Mining 6 Table 1.2 KDnuggets : Polls: Data Mining Software (May 2008) 13 Table 4.1 Questions to measure Loyalty 71 Table 4.2 Summarized Sample Statistics 73 Table 4.3 Sample Descriptive with Coding 74 Table 4.4 Chi-Square Test Illustration 81 Table 4.5 Color Preference by Customers for Car Dealership 83 Table 4.6 Directions for Setting up Worksheet for Chi-Square 84 Table 5.1 Auto-Clustering 100 Table 5.2 Cluster Distribution 101 Table 5.3 Store Loyalty amongst Surveyed Customers 102 Table 5.4 Profiling of Cluster by Gender 103 Table 5.5 Profiling of Cluster by Age 104 Table 5.6 Profiling of Cluster by Occupation 105 Table 5.7 Profiling of Cluster by Education 107 Table 5.8 Profiling of Cluster by Income 109 Table 5.9 Profiling of Cluster by Shop-with 110 Table 5.10 Profiling of Cluster by Expenditure 112 xii

12 Table 5.11 Profiling of Cluster by Trips 113 Table 5.12 Summary of Demographic/ Behavioral variables sample distribution and cluster membership 115 Table 5.13 Descriptive Statistics 118 Table 5.14 Correlation Matrix 120 Table 5.15 Anti-Image Matrix 122 Table 5.16 KMO and Bartlett s Test 124 Table 5.17 Communalities 126 Table 5.18 Total Variance Explained 127 Table 5.19 Component Matrix 129 Table 5.20 Rotated Component Matrix 130 Table 5.21 Short- Listed Attributes (Factor Loadings above.40) 131 Table 5.22 Component Transformation Matrix 132 Table 5.23 Factor Score Coefficient Matrix 133 Table 5.24 Factor Analysis of Grocery Store Attribute: Interpretation of Factors 134 Table 5.25 Reliability Analysis of Factors 135 Table 5.26 Variables Entered/ Removed 137 Table 5.27 Model Summary 138 Table 5.28 ANOVA 139 Table 5.29 Regression Coefficients 140 xiii

5.2 Customers Types for Grocery Shopping Scenario

5.2 Customers Types for Grocery Shopping Scenario ------------------------------------------------------------------------------------------------------- CHAPTER 5: RESULTS AND ANALYSIS -------------------------------------------------------------------------------------------------------

More information

Data Mining Techniques in CRM

Data Mining Techniques in CRM Data Mining Techniques in CRM Inside Customer Segmentation Konstantinos Tsiptsis CRM 6- Customer Intelligence Expert, Athens, Greece Antonios Chorianopoulos Data Mining Expert, Athens, Greece WILEY A John

More information

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19 PREFACE xi 1 INTRODUCTION 1 1.1 Overview 1 1.2 Definition 1 1.3 Preparation 2 1.3.1 Overview 2 1.3.2 Accessing Tabular Data 3 1.3.3 Accessing Unstructured Data 3 1.3.4 Understanding the Variables and Observations

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014 RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

More information

An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

More information

Chapter 12 Discovering New Knowledge Data Mining

Chapter 12 Discovering New Knowledge Data Mining Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to

More information

Federico Rajola. Customer Relationship. Management in the. Financial Industry. Organizational Processes and. Technology Innovation.

Federico Rajola. Customer Relationship. Management in the. Financial Industry. Organizational Processes and. Technology Innovation. Federico Rajola Customer Relationship Management in the Financial Industry Organizational Processes and Technology Innovation Second edition ^ Springer Contents 1 Introduction 1 1.1 Identification and

More information

Role of Social Networking in Marketing using Data Mining

Role of Social Networking in Marketing using Data Mining Role of Social Networking in Marketing using Data Mining Mrs. Saroj Junghare Astt. Professor, Department of Computer Science and Application St. Aloysius College, Jabalpur, Madhya Pradesh, India Abstract:

More information

A Review of Data Mining Techniques

A Review of Data Mining Techniques Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association

More information

LIST OF TABLES. 4.3 The frequency distribution of employee s opinion about training functions emphasizes the development of managerial competencies

LIST OF TABLES. 4.3 The frequency distribution of employee s opinion about training functions emphasizes the development of managerial competencies LIST OF TABLES Table No. Title Page No. 3.1. Scoring pattern of organizational climate scale 60 3.2. Dimension wise distribution of items of HR practices scale 61 3.3. Reliability analysis of HR practices

More information

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms Data Mining Techniques forcrm Data Mining The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. Extremely large datasets Discovery of the non-obvious Useful knowledge

More information

IT and CRM A basic CRM model Data source & gathering system Database system Data warehouse Information delivery system Information users

IT and CRM A basic CRM model Data source & gathering system Database system Data warehouse Information delivery system Information users 1 IT and CRM A basic CRM model Data source & gathering Database Data warehouse Information delivery Information users 2 IT and CRM Markets have always recognized the importance of gathering detailed data

More information

How to Get More Value from Your Survey Data

How to Get More Value from Your Survey Data Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2

More information

ANALYTICS CENTER LEARNING PROGRAM

ANALYTICS CENTER LEARNING PROGRAM Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

More information

Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

More information

List of Tables. Page Table Name Number. Number 2.1 Goleman's Emotional Intelligence Components 13 2.2 Components of TLQ 34 2.3

List of Tables. Page Table Name Number. Number 2.1 Goleman's Emotional Intelligence Components 13 2.2 Components of TLQ 34 2.3 xi List of s 2.1 Goleman's Emotional Intelligence Components 13 2.2 Components of TLQ 34 2.3 Components of Styles / Self Awareness Reviewed 51 2.4 Relationships to be studied between Self Awareness / Styles

More information

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA POLITECNICO DI MILANO GRADUATE SCHOOL OF BUSINESS BABD INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA Courses Description A JOINT PROGRAM WITH POLITECNICO DI MILANO SCHOOL OF MANAGEMENT PRE-COURSES

More information

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition

More information

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.

More information

An Introduction to Data Mining

An Introduction to Data Mining An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail

More information

CUSTOMER RELATIONSHIP MANAGEMENT AND ITS INFLUENCE ON CUSTOMER LOYALTY AT LIBERTY LIFE IN SOUTH AFRICA. Leon du Plessis MINOR DISSERTATION

CUSTOMER RELATIONSHIP MANAGEMENT AND ITS INFLUENCE ON CUSTOMER LOYALTY AT LIBERTY LIFE IN SOUTH AFRICA. Leon du Plessis MINOR DISSERTATION CUSTOMER RELATIONSHIP MANAGEMENT AND ITS INFLUENCE ON CUSTOMER LOYALTY AT LIBERTY LIFE IN SOUTH AFRICA by Leon du Plessis MINOR DISSERTATION Submitted in partial fulfilment of the requirements for the

More information

Customer and Business Analytic

Customer and Business Analytic Customer and Business Analytic Applied Data Mining for Business Decision Making Using R Daniel S. Putler Robert E. Krider CRC Press Taylor &. Francis Group Boca Raton London New York CRC Press is an imprint

More information

T-test & factor analysis

T-test & factor analysis Parametric tests T-test & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue

More information

Chapter 5. Warehousing, Data Acquisition, Data. Visualization

Chapter 5. Warehousing, Data Acquisition, Data. Visualization Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization 5-1 Learning Objectives

More information

Q1 Define the following: Data Mining, ETL, Transaction coordinator, Local Autonomy, Workload distribution

Q1 Define the following: Data Mining, ETL, Transaction coordinator, Local Autonomy, Workload distribution Q1 Define the following: Data Mining, ETL, Transaction coordinator, Local Autonomy, Workload distribution Q2 What are Data Mining Activities? Q3 What are the basic ideas guide the creation of a data warehouse?

More information

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS Mrs. Jyoti Nawade 1, Dr. Balaji D 2, Mr. Pravin Nawade 3 1 Lecturer, JSPM S Bhivrabai Sawant Polytechnic, Pune (India) 2 Assistant

More information

A New Approach for Evaluation of Data Mining Techniques

A New Approach for Evaluation of Data Mining Techniques 181 A New Approach for Evaluation of Data Mining s Moawia Elfaki Yahia 1, Murtada El-mukashfi El-taher 2 1 College of Computer Science and IT King Faisal University Saudi Arabia, Alhasa 31982 2 Faculty

More information

DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.

DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM. DATA MINING TECHNOLOGY Georgiana Marin 1 Abstract In terms of data processing, classical statistical models are restrictive; it requires hypotheses, the knowledge and experience of specialists, equations,

More information

Data Mining + Business Intelligence. Integration, Design and Implementation

Data Mining + Business Intelligence. Integration, Design and Implementation Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution

More information

KNOWLEDGE BASE DATA MINING FOR BUSINESS INTELLIGENCE

KNOWLEDGE BASE DATA MINING FOR BUSINESS INTELLIGENCE KNOWLEDGE BASE DATA MINING FOR BUSINESS INTELLIGENCE Dr. Ruchira Bhargava 1 and Yogesh Kumar Jakhar 2 1 Associate Professor, Department of Computer Science, Shri JagdishPrasad Jhabarmal Tibrewala University,

More information

------------------------------------------------------------------------------------------------------------ CHAPTER 3: DATA MINING: AN OVERVIEW ------------------------------------------------------------------------------------------------------------

More information

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP Data Warehousing and End-User Access Tools OLAP and Data Mining Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities. Key

More information

Easily Identify Your Best Customers

Easily Identify Your Best Customers IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do

More information

DATA MINING TECHNIQUES FOR IDENTIFYING THE CUSTOMER BEHAVIOUR OF INVESTMENT IN STOCK MARKET IN INDIA

DATA MINING TECHNIQUES FOR IDENTIFYING THE CUSTOMER BEHAVIOUR OF INVESTMENT IN STOCK MARKET IN INDIA DATA MINING TECHNIQUES FOR IDENTIFYING THE CUSTOMER BEHAVIOUR OF INVESTMENT IN STOCK MARKET IN INDIA DR. NAVEETA MEHTA*; MS. SHILPA DANG** * Associate Prof., MMICT&BM, MCA Dept., M.M. University, Mullana

More information

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

More information

Data Mining Solutions for the Business Environment

Data Mining Solutions for the Business Environment Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over

More information

Practical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING

Practical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING Practical Applications of DATA MINING Sang C Suh Texas A&M University Commerce r 3 JONES & BARTLETT LEARNING Contents Preface xi Foreword by Murat M.Tanik xvii Foreword by John Kocur xix Chapter 1 Introduction

More information

Introduction to Data Mining and Business Intelligence Lecture 1/DMBI/IKI83403T/MTI/UI

Introduction to Data Mining and Business Intelligence Lecture 1/DMBI/IKI83403T/MTI/UI Introduction to Data Mining and Business Intelligence Lecture 1/DMBI/IKI83403T/MTI/UI Yudho Giri Sucahyo, Ph.D, CISA (yudho@cs.ui.ac.id) Faculty of Computer Science, University of Indonesia Objectives

More information

Customer Analytics. Turn Big Data into Big Value

Customer Analytics. Turn Big Data into Big Value Turn Big Data into Big Value All Your Data Integrated in Just One Place BIRT Analytics lets you capture the value of Big Data that speeds right by most enterprises. It analyzes massive volumes of data

More information

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing

More information

Class 10. Data Mining and Artificial Intelligence. Data Mining. We are in the 21 st century So where are the robots?

Class 10. Data Mining and Artificial Intelligence. Data Mining. We are in the 21 st century So where are the robots? Class 1 Data Mining Data Mining and Artificial Intelligence We are in the 21 st century So where are the robots? Data mining is the one really successful application of artificial intelligence technology.

More information

Oracle Advanced Analytics 12c & SQLDEV/Oracle Data Miner 4.0 New Features

Oracle Advanced Analytics 12c & SQLDEV/Oracle Data Miner 4.0 New Features Oracle Advanced Analytics 12c & SQLDEV/Oracle Data Miner 4.0 New Features Charlie Berger, MS Eng, MBA Sr. Director Product Management, Data Mining and Advanced Analytics charlie.berger@oracle.com www.twitter.com/charliedatamine

More information

Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

More information

Title. Introduction to Data Mining. Dr Arulsivanathan Naidoo Statistics South Africa. OECD Conference Cape Town 8-10 December 2010.

Title. Introduction to Data Mining. Dr Arulsivanathan Naidoo Statistics South Africa. OECD Conference Cape Town 8-10 December 2010. Title Introduction to Data Mining Dr Arulsivanathan Naidoo Statistics South Africa OECD Conference Cape Town 8-10 December 2010 1 Outline Introduction Statistics vs Knowledge Discovery Predictive Modeling

More information

Sanjeev Kumar. contribute

Sanjeev Kumar. contribute RESEARCH ISSUES IN DATAA MINING Sanjeev Kumar I.A.S.R.I., Library Avenue, Pusa, New Delhi-110012 sanjeevk@iasri.res.in 1. Introduction The field of data mining and knowledgee discovery is emerging as a

More information

An Overview of Database management System, Data warehousing and Data Mining

An Overview of Database management System, Data warehousing and Data Mining An Overview of Database management System, Data warehousing and Data Mining Ramandeep Kaur 1, Amanpreet Kaur 2, Sarabjeet Kaur 3, Amandeep Kaur 4, Ranbir Kaur 5 Assistant Prof., Deptt. Of Computer Science,

More information

Effect of Business Value Chain Practices on the Supply Chain Performance of Large Manufacturing Firms in Kenya

Effect of Business Value Chain Practices on the Supply Chain Performance of Large Manufacturing Firms in Kenya Effect of Business Value Chain Practices on the Supply Chain Performance of Large Manufacturing Firms in Kenya TITLE Perris Wambui Chege A Research Proposal Submitted in Partial Fulfillment of Requirement

More information

Study and Analysis of Data Mining Concepts

Study and Analysis of Data Mining Concepts Study and Analysis of Data Mining Concepts M.Parvathi Head/Department of Computer Applications Senthamarai college of Arts and Science,Madurai,TamilNadu,India/ Dr. S.Thabasu Kannan Principal Pannai College

More information

Database Marketing, Business Intelligence and Knowledge Discovery

Database Marketing, Business Intelligence and Knowledge Discovery Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski

More information

Application of Predictive Model for Elementary Students with Special Needs in New Era University

Application of Predictive Model for Elementary Students with Special Needs in New Era University Application of Predictive Model for Elementary Students with Special Needs in New Era University Jannelle ds. Ligao, Calvin Jon A. Lingat, Kristine Nicole P. Chiu, Cym Quiambao, Laurice Anne A. Iglesia

More information

Statistics for BIG data

Statistics for BIG data Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before

More information

Analyzing Polls and News Headlines Using Business Intelligence Techniques

Analyzing Polls and News Headlines Using Business Intelligence Techniques Analyzing Polls and News Headlines Using Business Intelligence Techniques Eleni Fanara, Gerasimos Marketos, Nikos Pelekis and Yannis Theodoridis Department of Informatics, University of Piraeus, 80 Karaoli-Dimitriou

More information

Data Analysis. Management Information Systems 13

Data Analysis. Management Information Systems 13 Data Analysis Management Information Systems 13 166137-01+02 Management Information Systems Spring 2014 Sync Sangwon Lee, Ph. D D. of Information & Electronic Commerce WONKWANG University Prof. Dr. SSL

More information

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015 An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

More information

Data Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1

Data Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1 Data Mining 1 Introduction 2 Data Mining methods Alfred Holl Data Mining 1 1 Introduction 1.1 Motivation 1.2 Goals and problems 1.3 Definitions 1.4 Roots 1.5 Data Mining process 1.6 Epistemological constraints

More information

DATA MINING TECHNIQUES AND APPLICATIONS

DATA MINING TECHNIQUES AND APPLICATIONS DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,

More information

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

More information

Information Management course

Information Management course Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)

More information

Mobile Phone APP Software Browsing Behavior using Clustering Analysis

Mobile Phone APP Software Browsing Behavior using Clustering Analysis Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, January 7 9, 2014 Mobile Phone APP Software Browsing Behavior using Clustering Analysis

More information

Course Syllabus Business Intelligence and CRM Technologies

Course Syllabus Business Intelligence and CRM Technologies Course Syllabus Business Intelligence and CRM Technologies August December 2014 IX Semester Rolando Gonzales I. General characteristics Name : Business Intelligence CRM Technologies Code : 06063 Requirement

More information

Data Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland

Data Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland Data Mining and Knowledge Discovery in Databases (KDD) State of the Art Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland 1 Conference overview 1. Overview of KDD and data mining 2. Data

More information

DATA ANALYTICS USING R

DATA ANALYTICS USING R DATA ANALYTICS USING R Duration: 90 Hours Intended audience and scope: The course is targeted at fresh engineers, practicing engineers and scientists who are interested in learning and understanding data

More information

ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies

ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

More information

3/17/2009. Knowledge Management BIKM eclassifier Integrated BIKM Tools

3/17/2009. Knowledge Management BIKM eclassifier Integrated BIKM Tools Paper by W. F. Cody J. T. Kreulen V. Krishna W. S. Spangler Presentation by Dylan Chi Discussion by Debojit Dhar THE INTEGRATION OF BUSINESS INTELLIGENCE AND KNOWLEDGE MANAGEMENT BUSINESS INTELLIGENCE

More information

Data Mining Techniques for Banking Applications

Data Mining Techniques for Banking Applications International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Volume 2, Issue 4, April 2015, PP 15-20 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org Data

More information

A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication

A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication 2012 45th Hawaii International Conference on System Sciences A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication Namhyoung Kim, Jaewook Lee Department of Industrial and Management

More information

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Data Mining for Customer Service Support Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Traditional Hotline Services Problem Traditional Customer Service Support (manufacturing)

More information

Fluency With Information Technology CSE100/IMT100

Fluency With Information Technology CSE100/IMT100 Fluency With Information Technology CSE100/IMT100 ),7 Larry Snyder & Mel Oyler, Instructors Ariel Kemp, Isaac Kunen, Gerome Miklau & Sean Squires, Teaching Assistants University of Washington, Autumn 1999

More information

Enhanced Boosted Trees Technique for Customer Churn Prediction Model

Enhanced Boosted Trees Technique for Customer Churn Prediction Model IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 03 (March. 2014), V5 PP 41-45 www.iosrjen.org Enhanced Boosted Trees Technique for Customer Churn Prediction

More information

2015 Workshops for Professors

2015 Workshops for Professors SAS Education Grow with us Offered by the SAS Global Academic Program Supporting teaching, learning and research in higher education 2015 Workshops for Professors 1 Workshops for Professors As the market

More information

CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics

CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics Session map Session1 Session 2 Introduction The new focus on customer loyalty CRM and Business Intelligence CRM Marketing initiatives Session

More information

The University of Jordan

The University of Jordan The University of Jordan Master in Web Intelligence Non Thesis Department of Business Information Technology King Abdullah II School for Information Technology The University of Jordan 1 STUDY PLAN MASTER'S

More information

Data Mining: Overview. What is Data Mining?

Data Mining: Overview. What is Data Mining? Data Mining: Overview What is Data Mining? Recently * coined term for confluence of ideas from statistics and computer science (machine learning and database methods) applied to large databases in science,

More information

Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier

Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier Data Mining: Concepts and Techniques Jiawei Han Micheline Kamber Simon Fräser University К MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF Elsevier Contents Foreword Preface xix vii Chapter I Introduction I I.

More information

Delivering Business Intelligence With Microsoft SQL Server 2005 or 2008 HDT922 Five Days

Delivering Business Intelligence With Microsoft SQL Server 2005 or 2008 HDT922 Five Days or 2008 Five Days Prerequisites Students should have experience with any relational database management system as well as experience with data warehouses and star schemas. It would be helpful if students

More information

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA ABSTRACT Current trends in data mining allow the business community to take advantage of

More information

MBA 8473 - Data Mining & Knowledge Discovery

MBA 8473 - Data Mining & Knowledge Discovery MBA 8473 - Data Mining & Knowledge Discovery MBA 8473 1 Learning Objectives 55. Explain what is data mining? 56. Explain two basic types of applications of data mining. 55.1. Compare and contrast various

More information

Succession planning in Chinese family-owned businesses in Hong Kong: an exploratory study on critical success factors and successor selection criteria

Succession planning in Chinese family-owned businesses in Hong Kong: an exploratory study on critical success factors and successor selection criteria Succession planning in Chinese family-owned businesses in Hong Kong: an exploratory study on critical success factors and successor selection criteria By Ling Ming Chan BEng (University of Newcastle upon

More information

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be

More information

Data Mining for Fun and Profit

Data Mining for Fun and Profit Data Mining for Fun and Profit Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. - Ian H. Witten, Data Mining: Practical Machine Learning Tools

More information

Data Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.

Data Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds. Sept 03-23-05 22 2005 Data Mining for Model Creation Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.com page 1 Agenda Data Mining and Estimating Model Creation

More information

TNS EX A MINE BehaviourForecast Predictive Analytics for CRM. TNS Infratest Applied Marketing Science

TNS EX A MINE BehaviourForecast Predictive Analytics for CRM. TNS Infratest Applied Marketing Science TNS EX A MINE BehaviourForecast Predictive Analytics for CRM 1 TNS BehaviourForecast Why is BehaviourForecast relevant for you? The concept of analytical Relationship Management (acrm) becomes more and

More information

Data Mining Analytics for Business Intelligence and Decision Support

Data Mining Analytics for Business Intelligence and Decision Support Data Mining Analytics for Business Intelligence and Decision Support Chid Apte, T.J. Watson Research Center, IBM Research Division Knowledge Discovery and Data Mining (KDD) techniques are used for analyzing

More information

STATISTICA. Financial Institutions. Case Study: Credit Scoring. and

STATISTICA. Financial Institutions. Case Study: Credit Scoring. and Financial Institutions and STATISTICA Case Study: Credit Scoring STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table of Contents INTRODUCTION: WHAT

More information

Factors Influencing the Adoption of Biometric Authentication in Mobile Government Security

Factors Influencing the Adoption of Biometric Authentication in Mobile Government Security Factors Influencing the Adoption of Biometric Authentication in Mobile Government Security Thamer Omar Alhussain Bachelor of Computing, Master of ICT School of Information and Communication Technology

More information

WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat

WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat Information Builders enables agile information solutions with business intelligence (BI) and integration technologies. WebFOCUS the most widely utilized business intelligence platform connects to any enterprise

More information

Course Syllabus For Operations Management. Management Information Systems

Course Syllabus For Operations Management. Management Information Systems For Operations Management and Management Information Systems Department School Year First Year First Year First Year Second year Second year Second year Third year Third year Third year Third year Third

More information

HAROLD CAMPING i ii iii iv v vi vii viii ix x xi xii 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

More information

Data Warehousing and Data Mining in Business Applications

Data Warehousing and Data Mining in Business Applications 133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business

More information

not possible or was possible at a high cost for collecting the data.

not possible or was possible at a high cost for collecting the data. Data Mining and Knowledge Discovery Generating knowledge from data Knowledge Discovery Data Mining White Paper Organizations collect a vast amount of data in the process of carrying out their day-to-day

More information

Clustering Methods in Data Mining with its Applications in High Education

Clustering Methods in Data Mining with its Applications in High Education 2012 International Conference on Education Technology and Computer (ICETC2012) IPCSIT vol.43 (2012) (2012) IACSIT Press, Singapore Clustering Methods in Data Mining with its Applications in High Education

More information

EXPERT SYSTEM FOR RESOLUTION OF DELAY CLAIMS IN CONSTRUCTION CONTRACTS

EXPERT SYSTEM FOR RESOLUTION OF DELAY CLAIMS IN CONSTRUCTION CONTRACTS EXPERT SYSTEM FOR RESOLUTION OF DELAY CLAIMS IN CONSTRUCTION CONTRACTS by NITIN BALKRISHNA CHAPHALKAR Department of Civil Engineering Submitted in fulfillment of the requirements of the degree of Doctor

More information

A supply chain analytics approach to product assortment optimization

A supply chain analytics approach to product assortment optimization A supply chain analytics approach to product assortment optimization Rajesh Kumar (rajesh.kumar7@hp.com), Vishwanathan Rajagopalan, Jayesh Baldania, Nidhi Sagar, Santanu Sinha, Priyanka Dahiya, Jyotirmay

More information

Statistical Models in Data Mining

Statistical Models in Data Mining Statistical Models in Data Mining Sargur N. Srihari University at Buffalo The State University of New York Department of Computer Science and Engineering Department of Biostatistics 1 Srihari Flood of

More information

Web Data Mining: A Case Study. Abstract. Introduction

Web Data Mining: A Case Study. Abstract. Introduction Web Data Mining: A Case Study Samia Jones Galveston College, Galveston, TX 77550 Omprakash K. Gupta Prairie View A&M, Prairie View, TX 77446 okgupta@pvamu.edu Abstract With an enormous amount of data stored

More information

Distance Learning and Examining Systems

Distance Learning and Examining Systems Lodz University of Technology Distance Learning and Examining Systems - Theory and Applications edited by Sławomir Wiak Konrad Szumigaj HUMAN CAPITAL - THE BEST INVESTMENT The project is part-financed

More information

Introduction. A. Bellaachia Page: 1

Introduction. A. Bellaachia Page: 1 Introduction 1. Objectives... 3 2. What is Data Mining?... 4 3. Knowledge Discovery Process... 5 4. KD Process Example... 7 5. Typical Data Mining Architecture... 8 6. Database vs. Data Mining... 9 7.

More information

STUDENTS ATTITUDES TOWARDS BUSINESS ETHICS: A COMPARISON BETWEEN INDONESIA AND LESOTHO.

STUDENTS ATTITUDES TOWARDS BUSINESS ETHICS: A COMPARISON BETWEEN INDONESIA AND LESOTHO. i THESIS STUDENTS ATTITUDES TOWARDS BUSINESS ETHICS: A COMPARISON BETWEEN INDONESIA AND LESOTHO. MPHOLLE CLEMENT PAE-PAE Student ID Number :125001758/PS/MM MASTER OF MANAGEMENT PROGRAM POSTGRADUATE PROGRAM

More information

Course Syllabus. Purposes of Course:

Course Syllabus. Purposes of Course: Course Syllabus Eco 5385.701 Predictive Analytics for Economists Summer 2014 TTh 6:00 8:50 pm and Sat. 12:00 2:50 pm First Day of Class: Tuesday, June 3 Last Day of Class: Tuesday, July 1 251 Maguire Building

More information