# Data Mining. Shahram Hassas Math 382 Professor: Shapiro

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Data Mining Shahram Hassas Math 382 Professor: Shapiro

2 Agenda Introduction Major Elements Steps/ Processes Examples Tools used for data mining Advantages and Disadvantages

3 What is Data Mining? Described as the method of comparing large volumes of data looking for more information from a data Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information which can be used to increase revenue, and cut costs.

4 What is Data Mining? Data mining is the results of Classical statistics, artificial intelligence and machine learning. Classical statistic plays the main role in data mining. Artificial intelligence applies human interest processing to statistical problems.

5 What is it used for? To assist in the analysis of collections of observations and finding correlations among all of the fields and relationship between the facts in databases.

6 Examples grocery chain stores discovered that when men bought diapers on Thursdays and Saturdays, they also attempt to buy beer. It is also showed that these shoppers typically did their weekly grocery shopping on Saturdays and only bought a few items on Thursdays. The grocery chain used this newly discovered information in various ways to increase revenue for instance; they moved the beers closer to the diapers and, they could make sure beer and diapers were sold at full price on Thursdays.

7 Data mining consists of five major elements: Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a multidimensional database system. Provide data access to business analysts and information technology professionals. Analyze the data by application software. Present the data in a useful format, such as a graph or table.

8 Data Mining Goal Data mining application goals are predictions, and it allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships by exploration to estimate the expected result(s).

9 Examples The National Basketball Association (NBA) is exploring a data mining application that can combine and analyze the records of basketball games by using advanced Scout software. This program helps to find patterns derived from game statistics, images, and the movements of the players themselves.

10 3 Steps Data Mining Process Stage 1: Exploration. This stage usually starts with data preparation which may involve cleaning data, data transformations, selecting subsets of records Stage 2: Model building and validation. This stage involves considering various models and choosing the best one based on their predictive performance Stage 3: Deployment. That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome

11 Some of the tools used for data mining are: Artificial neural networks - Non-linear predictive models that learn through training and resemble biological neural networks in structure. Decision trees - Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Rule induction - The extraction of useful if-then rules from data based on statistical significance. Genetic algorithms - Optimization techniques based on the concepts of genetic combination, mutation, and natural selection. Nearest neighbor - A classification technique that classifies each record based on the records most similar to it in an historical database.

12 Reasons for the growing popularity of Data Mining Growing Data Volume Limitations of Human Analysis Cost and efficiency compared to other Statistical data applications

13 ADVANTAGES OF DATA MINING Data mining has been used in area of science and engineering, such as bioinformatics, genetics, medicine, education, agricultural, electrical power engineering, and law enforcement

14 ADVANTAGES OF DATA MINING In the field of human genetics, usage of data mining attempts to find out how the changes in an individual's DNA sequence affect the risk of developing common diseases such as cancer.

15 ADVANTAGES OF DATA MINING Law enforcement: Data mining can aid law enforcers in identifying criminal suspects. Electrical power Engineering: data mining techniques have been used for condition monitoring of high voltage electrical equipment to obtain valuable information on the insulation's health status of the equipment

16 DISADVANTAGES OF DATA MINING Privacy Issues: For example, according to the Washington Post, in 1998, CVS had sold their patient s prescription purchases to a different company American Express also sold their customers credit card purchases to another company.

17 DISADVANTAGES OF DATA MINING Cont Security issues: Although companies have a lot of personal information about us available online, they do not have sufficient security systems in place to protect that information. Misuse of information: Some of the companies will answer your phone call based on your purchase history. If you have spent a lot of money or bought a lot of products from one company, your call will be answered really soon. So you should not think that your call is really being answered in the order in which it was received.

18 References: alace/datamining.htm/data_mining.htm, palace. Palace, Bill. Data Mining.

### 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,

### 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,

### 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

### DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM

INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM M. Mayilvaganan 1, S. Aparna 2 1 Associate

### 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

### 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

### Session 10 : E-business models, Big Data, Data Mining, Cloud Computing

INFORMATION STRATEGY Session 10 : E-business models, Big Data, Data Mining, Cloud Computing Tharaka Tennekoon B.Sc (Hons) Computing, MBA (PIM - USJ) POST GRADUATE DIPLOMA IN BUSINESS AND FINANCE 2014 Internet

### 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

### 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

### Data Mining for Successful Healthcare Organizations

Data Mining for Successful Healthcare Organizations For successful healthcare organizations, it is important to empower the management and staff with data warehousing-based critical thinking and knowledge

### 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,

### An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. XML data mining research based on multi-level technology

[Type text] [Type text] [Type text] ISSN : 0974-7435 Volume 10 Issue 13 BioTechnology 2014 An Indian Journal FULL PAPER BTAIJ, 10(13), 2014 [7602-7609] XML data mining research based on multi-level technology

### 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

### A Survey on Web Research for Data Mining

A Survey on Web Research for Data Mining Gaurav Saini 1 gauravhpror@gmail.com 1 Abstract Web mining is the application of data mining techniques to extract knowledge from web data, including web documents,

### Machine Learning and Data Mining. Fundamentals, robotics, recognition

Machine Learning and Data Mining Fundamentals, robotics, recognition Machine Learning, Data Mining, Knowledge Discovery in Data Bases Their mutual relations Data Mining, Knowledge Discovery in Databases,

### 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

### Data Mining Submitted in partial fulfillment of the requirement for the award of degree Of Computer Science

A Seminar report On Data Mining Submitted in partial fulfillment of the requirement for the award of degree Of Computer Science SUBMITTED TO: www.studymafia.org SUBMITTED BY: www.studymafia.org Preface

### CHAPTER 3 DATA MINING AND CLUSTERING

CHAPTER 3 DATA MINING AND CLUSTERING 3.1 Introduction Nowadays, large quantities of data are being accumulated. The amount of data collected is said to be almost doubled every 9 months. Seeking knowledge

### Introduction to Data Mining

Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:

### 15.564 Information Technology I. Business Intelligence

15.564 Information Technology I Business Intelligence Outline Operational vs. Decision Support Systems What is Data Mining? Overview of Data Mining Techniques Overview of Data Mining Process Data Warehouses

### 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?

### An Introduction to Advanced Analytics and Data Mining

An Introduction to Advanced Analytics and Data Mining Dr Barry Leventhal Henry Stewart Briefing on Marketing Analytics 19 th November 2010 Agenda What are Advanced Analytics and Data Mining? The toolkit

### Data Mining and Machine Learning in Bioinformatics

Data Mining and Machine Learning in Bioinformatics PRINCIPAL METHODS AND SUCCESSFUL APPLICATIONS Ruben Armañanzas http://mason.gmu.edu/~rarmanan Adapted from Iñaki Inza slides http://www.sc.ehu.es/isg

### 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

### Nine Common Types of Data Mining Techniques Used in Predictive Analytics

1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better

### 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.

### 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

### Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of

### 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

### In this presentation, you will be introduced to data mining and the relationship with meaningful use.

In this presentation, you will be introduced to data mining and the relationship with meaningful use. Data mining refers to the art and science of intelligent data analysis. It is the application of machine

### A HOLISTIC FRAMEWORK FOR KNOWLEDGE MANAGEMENT

A HOLISTIC FRAMEWORK FOR KNOWLEDGE MANAGEMENT Dr. Shamsul Chowdhury, Roosevelt University, schowdhu@roosevelt.edu ABSTRACT Knowledge management refers to the set of processes developed in an organization

### 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

### Data Mining. Anyone can tell you that it takes hard work, talent, and hours upon hours of

Seth Rhine Math 382 Shapiro Data Mining Anyone can tell you that it takes hard work, talent, and hours upon hours of watching videos for a professional sports team to be successful. Finding the leaks in

### SEEM Introduction to Knowledge Systems and Machine Learning

SEEM 5470 Introduction to Knowledge Systems and Machine Learning Data vs. Knowledge Society produces huge amounts of data sources: business, science, medicine, economics, geography, environment, sports,

### 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)

### 2.1. Data Mining for Biomedical and DNA data analysis

Applications of Data Mining Simmi Bagga Assistant Professor Sant Hira Dass Kanya Maha Vidyalaya, Kala Sanghian, Distt Kpt, India (Email: simmibagga12@gmail.com) Dr. G.N. Singh Department of Physics and

### OLAP and Data Mining. OLTP Compared With OLAP. The Internet Grocer. Chapter 17

OLAP and Data Mining Chapter 17 OLTP Compared With OLAP On Line Transaction Processing OLTP Maintains a database that is an accurate model of some realworld enterprise. Supports day-to-day operations.

### 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

### CONTEMPORARY DECISION SUPPORT AND KNOWLEDGE MANAGEMENT TECHNOLOGIES

I International Symposium Engineering Management And Competitiveness 2011 (EMC2011) June 24-25, 2011, Zrenjanin, Serbia CONTEMPORARY DECISION SUPPORT AND KNOWLEDGE MANAGEMENT TECHNOLOGIES Slavoljub Milovanovic

### Assessing Data Mining: The State of the Practice

Assessing Data Mining: The State of the Practice 2003 Herbert A. Edelstein Two Crows Corporation 10500 Falls Road Potomac, Maryland 20854 www.twocrows.com (301) 983-3555 Objectives Separate myth from reality

### Using Artificial Intelligence to Manage Big Data for Litigation

FEBRUARY 3 5, 2015 / THE HILTON NEW YORK Using Artificial Intelligence to Manage Big Data for Litigation Understanding Artificial Intelligence to Make better decisions Improve the process Allay the fear

### Example application (1) Telecommunication. Lecture 1: Data Mining Overview and Process. Example application (2) Health

Lecture 1: Data Mining Overview and Process What is data mining? Example applications Definitions Multi disciplinary Techniques Major challenges The data mining process History of data mining 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

### Search and Data Mining: Techniques. Applications Anya Yarygina Boris Novikov

Search and Data Mining: Techniques Applications Anya Yarygina Boris Novikov Introduction Data mining applications Data mining system products and research prototypes Additional themes on data mining Social

### A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH

205 A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH ABSTRACT MR. HEMANT KUMAR*; DR. SARMISTHA SARMA** *Assistant Professor, Department of Information Technology (IT), Institute of Innovation in Technology

### Customer Classification And Prediction Based On Data Mining Technique

Customer Classification And Prediction Based On Data Mining Technique Ms. Neethu Baby 1, Mrs. Priyanka L.T 2 1 M.E CSE, Sri Shakthi Institute of Engineering and Technology, Coimbatore 2 Assistant Professor

### Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD

Predictive Analytics Techniques: What to Use For Your Big Data March 26, 2014 Fern Halper, PhD Presenter Proven Performance Since 1995 TDWI helps business and IT professionals gain insight about data warehousing,

### Foundations of Artificial Intelligence. Introduction to Data Mining

Foundations of Artificial Intelligence Introduction to Data Mining Objectives Data Mining Introduce a range of data mining techniques used in AI systems including : Neural networks Decision trees Present

### Chapter 20: Data Analysis

Chapter 20: Data Analysis Database System Concepts, 6 th Ed. See www.db-book.com for conditions on re-use Chapter 20: Data Analysis Decision Support Systems Data Warehousing Data Mining Classification

### Data Mining Applications in Higher Education

Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2

### ECLT 5810 E-Commerce Data Mining Techniques - Introduction. Prof. Wai Lam

ECLT 5810 E-Commerce Data Mining Techniques - Introduction Prof. Wai Lam Data Opportunities Business infrastructure have improved the ability to collect data Virtually every aspect of business is now open

### 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

### 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

### III JORNADAS DE DATA MINING

III JORNADAS DE DATA MINING EN EL MARCO DE LA MAESTRÍA EN DATA MINING DE LA UNIVERSIDAD AUSTRAL PRESENTACIÓN TECNOLÓGICA IBM Alan Schcolnik, Cognos Technical Sales Team Leader, IBM Software Group. IAE

### Using Data Mining for Mobile Communication Clustering and Characterization

Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer

### 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

### 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.

### MS1b Statistical Data Mining

MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to

### Web Usage Mining: Identification of Trends Followed by the user through Neural Network

International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 617-624 International Research Publications House http://www. irphouse.com /ijict.htm Web

### 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

### INVESTIGATIONS INTO EFFECTIVENESS OF GAUSSIAN AND NEAREST MEAN CLASSIFIERS FOR SPAM DETECTION

INVESTIGATIONS INTO EFFECTIVENESS OF AND CLASSIFIERS FOR SPAM DETECTION Upasna Attri C.S.E. Department, DAV Institute of Engineering and Technology, Jalandhar (India) upasnaa.8@gmail.com Harpreet Kaur

### 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

Business Intelligence Overview What Is Business Intelligence? Its roots go back to the late 1960s In the 1970s, there were decision support systems (DSS) In the 1980s, there were EIS, OLAP, GIS, and more

### DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support

DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support Rok Rupnik, Matjaž Kukar, Marko Bajec, Marjan Krisper University of Ljubljana, Faculty of Computer and Information

### Data Isn't Everything

June 17, 2015 Innovate Forward Data Isn't Everything The Challenges of Big Data, Advanced Analytics, and Advance Computation Devices for Transportation Agencies. Using Data to Support Mission, Administration,

### Data Mining for Retail Website Design and Enhanced Marketing

Data Mining for Retail Website Design and Enhanced Marketing Inaugural-Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf

### International Journal of World Research, Vol: I Issue XIII, December 2008, Print ISSN: 2347-937X DATA MINING TECHNIQUES AND STOCK MARKET

DATA MINING TECHNIQUES AND STOCK MARKET Mr. Rahul Thakkar, Lecturer and HOD, Naran Lala College of Professional & Applied Sciences, Navsari ABSTRACT Without trading in a stock market we can t understand

### Intellectual Property / Copyright Material

What is Data Mining? Author: BALWANT RAI Organization: Evaltech, Inc. Evaltech Research Group, Data Warehousing Practice. Date: 02/27/04 Email: erg@evaltech.com Abstract: In this paper we will be going

### Subject Description Form

Subject Description Form Subject Code Subject Title COMP417 Data Warehousing and Data Mining Techniques in Business and Commerce Credit Value 3 Level 4 Pre-requisite / Co-requisite/ Exclusion Objectives

### Statistics and Data Mining

Statistics and Data Mining A B M Shawkat Ali PowerPoint permissions Cengage Learning Australia hereby permits the usage and posting of our copyright controlled PowerPoint slide content for all courses

### International Journal of Electronics and Computer Science Engineering 1449

International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN- 2277-1956 Neural Networks in Data Mining Priyanka Gaur Department of Information and

### INTRODUCTION TO MACHINE LEARNING 3RD EDITION

ETHEM ALPAYDIN The MIT Press, 2014 Lecture Slides for INTRODUCTION TO MACHINE LEARNING 3RD EDITION alpaydin@boun.edu.tr http://www.cmpe.boun.edu.tr/~ethem/i2ml3e CHAPTER 1: INTRODUCTION Big Data 3 Widespread

### Health Spring Meeting May 2008 Session # 42: Dental Insurance What's New, What's Important

Health Spring Meeting May 2008 Session # 42: Dental Insurance What's New, What's Important Floyd Ray Martin, FSA, MAAA Thomas A. McInteer, FSA, MAAA Jonathan P. Polon, FSA Dental Insurance Fraud Detection

### from Larson Text By Susan Miertschin

Decision Tree Data Mining Example from Larson Text By Susan Miertschin 1 Problem The Maximum Miniatures Marketing Department wants to do a targeted mailing gpromoting the Mythic World line of figurines.

### MA2823: Foundations of Machine Learning

MA2823: Foundations of Machine Learning École Centrale Paris Fall 2015 Chloé-Agathe Azencot Centre for Computational Biology, Mines ParisTech chloe agathe.azencott@mines paristech.fr TAs: Jiaqian Yu jiaqian.yu@centralesupelec.fr

### Data Mining & Knowledge Discovery: Personalization and Profiling Technologies

Data Mining & Knowledge Discovery: Personalization and Profiling Technologies 1 Predictive Modeling and Knowledge Discovery via Data Mining v A black box that makes predictions about the future based on

### 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,

### A Survey on Intrusion Detection System with Data Mining Techniques

A Survey on Intrusion Detection System with Data Mining Techniques Ms. Ruth D 1, Mrs. Lovelin Ponn Felciah M 2 1 M.Phil Scholar, Department of Computer Science, Bishop Heber College (Autonomous), Trichirappalli,

### ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies

ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

### Data Mining and Neural Networks in Stata

Data Mining and Neural Networks in Stata 2 nd Italian Stata Users Group Meeting Milano, 10 October 2005 Mario Lucchini e Maurizo Pisati Università di Milano-Bicocca mario.lucchini@unimib.it maurizio.pisati@unimib.it

### 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

### 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.

### Data Mining Part 5. Prediction

Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

### 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

### Building Data Warehousing and Data Mining from Course Management Systems: A Case Study of FUTA Course Management Information Systems

Building Data Warehousing and Data Mining from Course Management Systems: A Case Study of FUTA Course Management Information Systems *Akintola K.G., ** Adetunmbi A.O. **Adeola O.S. *Computer Science Department,

### The Data Mining Process

Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data

### Data Mining System, Functionalities and Applications: A Radical Review

Data Mining System, Functionalities and Applications: A Radical Review Dr. Poonam Chaudhary System Programmer, Kurukshetra University, Kurukshetra Abstract: Data Mining is the process of locating potentially

### A Survey on Data Mining with Big data - Applications, Techniques, Tools, Challenges and Visualization

A Survey on Data Mining with Big data - Applications, Techniques, Tools, Challenges and Visualization S. Usharani 1, K. Kungumaraj 2 Research Scholar, Department of Computer Science, Mother Teresa Women

### Feature Selection with Data Balancing for. Prediction of Bank Telemarketing

Applied Mathematical Sciences, Vol. 8, 2014, no. 114, 5667-5672 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.47222 Feature Selection with Data Balancing for Prediction of Bank Telemarketing

### 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

### 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

### 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,

### Outline. What is Big data and where they come from? How we deal with Big data?

What is Big Data Outline What is Big data and where they come from? How we deal with Big data? Big Data Everywhere! As a human, we generate a lot of data during our everyday activity. When you buy something,

### Analytical Approach for Bot Cheating Detection in a Massive Multiplayer Online Racing Game

Paper PO-13 Analytical Approach for Bot Cheating Detection in a Massive Multiplayer Online Racing Game Andrea Villanes, North Carolina State University ABSTRACT The videogame industry is a growing business

### Data Warehousing Dashboards & Data Mining. Empowering Extraordinary Patient Care

Data Warehousing Dashboards & Data Mining Empowering Extraordinary Patient Care Your phone has been automatically muted. Please use the Q&A panel to ask questions during the presentation. Introduction

### 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)

### Introduction to Data Mining

Introduction to Data Mining a.j.m.m. (ton) weijters (slides are partially based on an introduction of Gregory Piatetsky-Shapiro) Overview Why data mining (data cascade) Application examples Data Mining

### Data Mining and Statistics for Decision Making. Wiley Series in Computational Statistics

Brochure More information from http://www.researchandmarkets.com/reports/2171080/ Data Mining and Statistics for Decision Making. Wiley Series in Computational Statistics Description: Data Mining and Statistics