Contents. Dedication List of Figures List of Tables. Acknowledgments

Size: px
Start display at page:

Download "Contents. Dedication List of Figures List of Tables. Acknowledgments"

Transcription

1 Contents Dedication List of Figures List of Tables Foreword Preface Acknowledgments v xiii xvii xix xxi xxv Part I Concepts and Techniques 1. INTRODUCTION 3 1 The Quest for Knowledge 3 2 Problem Description 4 3 Related Bibliography 5 4 Scope of the Book 6 5 Contents of the Book 8 6 How to Read this Book 9 2. DATA MINING AND KNOWLEDGE DISCOVERY: A BRIEF OVERVIEW 11 1 History and Motivation The Emergence of Data Mining So, what is Data Mining? The KDD Process Organizing Data Mining Techniques 15 2 Data Preprocessing The Scope of Data Preprocessing Data Cleaning 18

2 viii A GENT INTELLIGENCE THR 0 UGH DA TA MINING 2.3 Data Integration Data Transformation Data Reduction Data Discretization 20 3 Classification and Prediction Defining Classification Bayesian Classification Decision Trees The ID3 algorithm 24 4 Clustering Definitions Clustering Techniques Representative Clustering Algorithms Partitioning Algorithms Hierarchical Algorithms Density-Based Algorithms 30 5 Association Rule Extraction Definitions Representative Algorithms 33 6 Evolutionary Data Mining Algorithms The Basic Concepts of Genetic Algorithms Genetic Algorithm Terminology Genetic Algorithm Operands The Genetic Algorithm Mechanism Application of Genetic Algorithms 38 7 Chapter review INTELLIGENT AGENTS AND MULTI-AGENT SYSTEMS 41 1 Intelligent Agents Agent Definition Agent Features and Working Definitions Agent Classification Agents and Objects Agents and Expert Systems Agent Programming Languages 47 2 Multi-Agent Systems Multi-Agent System Characteristics Agent Communication Agent Communication Languages 53

3 Contents KQML KIF FIPA ACL Agent Communities ix Part II Methodology 4. EXPLOITING DATA MINING ON MAS 59 1 Introduction Logic and Limitations Agent Training and Knowledge Diffusion Three Levels of Knowledge Diffusion for MAS 63 2 MAS Development Tools 63 3 Agent Academy A A Architecture Developing Multi-Agent Applications Creating Agent Ontologies Creating Behavior Types Creating Agent Types Deploying a Multi Agent System COUPLING DATA MINING WITH INTELLIGENT AGENTS 71 1 The Unified Methodology Formal Model Case 1: Training at the MAS application level Case 2: Training at the MAS behavior level Case 3: Training evolutionary agent communities Common Primitives for MAS Development Application Level: The Training Framework Behavior Level: The Training Framework Evolutionary Level: The Training Framework 80 2 Data Miner: A Tool for Training and Retraining Agents Prerequisites for Using the Data Miner Data Miner Overview Selection of the Appropriate DM Technique Training and Retraining with the Data Miner 86

4 x AGENT INTELLIGENCE THROUGH DATA MINING Part III Knowledge Diffusion: Three Representative Test Cases 6. DATA MINING ON THE APPLICATION LEVEL OF A MAS 93 1 Enterprise Resource Planning Systems 93 2 The Generalized Framework IRF Architecture Customer Order Agent type Recommendation Agent type Customer Profile Identification Agent type Supplier Pattern Identification Agent type Inventory Profile Identification Agent type Enterprise Resource Planning Agent type Installation and Runtime Workflows System Intelligence Benchmarking customer and suppliers IPIA products profile RA Intelligence An IRF Demonstrator Conclusions MINING AGENT BEHAVIORS Predicting Agent Behavior The Prediction Mechanism Applying «-Profile on MAS Modeling Agent Actions in an Operation Cycle Mapping Agent Actions to Vectors Evaluating Efficiency Profile efficiency evaluation Prediction system efficiency evaluation A Recommendation Engine Demonstrator System Parameters The fuzzy variable Time The fuzzy variable Frequency The output fuzzy variable Weight The Rules of the FIS Browsing through a Web Site Experimental Results Conclusions 133

5 Contents 3. MINING KNOWLEDGE FOR AGENT COMMUNITIES 1 Ecosystem Simulation 2 An Overview of Biotope 2.1 The Biotope Environment 2.2 The Biotope Agents Agent sight Agent movement Agent reproduction Agent communication - Knowledge exchange 2.3 Knowledge Extraction and Improvement Classifiers Classifier Evaluation mechanism Genetic Algorithm 2.4 The Assessment Indicators Environmental indicators Agent performance indicators 3 The Implemented Prototype 3.1 Creating a New Simulation Scenario 4 Experimental Results 4.1 Exploiting the Potential of Agent Communication Specifying the optimal communication rate Agent efficiency and knowledge base size Agent communication and unreliability 4.2 GAs in Unreliable Environments 4.3 Simulating Various Environments 5 Conclusions xi Part IV Extensions AGENT RETRAINING AND DYNAMICAL IMPROVEMENT OF AGENT INTELLIGENCE Formal Model Different Retraining Approaches Retraining in the Case of Classification Techniques Initial Training Retraining an Agent Type Retraining an Agent Instance Retraining in the Case of Clustering Techniques 169

6 xii AGENT INTELLIGENCE THROUGH DATA MINING 3.1 Initial Training Retraining Retraining in the Case of Association Rule Extraction Techniques Initial Training Retraining Retraining in the Case of Genetic Algorithms Experimental Results Intelligent Environmental Monitoring System Speech Recognition Agents The Iris Recommendation Agent Conclusions AREAS OF APPLICATION & FUTURE DIRECTIONS Areas of Application Environmental Monitoring Information Systems Agent Bidding and Auctioning Enhanced Software Processing Advanced AT-DM Symbiosis Architectures Distributed Agent Training Architectures Semantically-Aware Grid Architectures Summary and Conclusions Open Issues and Future Directions 185 References 189 Index 199 About The Authors 201

7 List of Figures 1.1 Mining for intelligence Agent-based applications and inference mechanisms Alternative routes for reading this book Technology evolution towards Data Mining A schematic representation of the KDD process The confluence of different technologies into DM A sample decision tree Deciding on the root node The clustering concept Intra- and inter-cluster similarity K-Means schematic representation The concepts of DBSCAN The Apriori algorithm Chromosome crossover Chromosome mutation The genetic algorithm mechanism The Nwana agent classification Alternative agent coordination schemes The structure of the reasoning agent Diagram of the Agent Academy development framework Creating the behavior of an agent through the Behavior Design Tool The unified MAS methodology The MAS development mechanism The common MAS development steps 75

8 xi v A GENT INTELLIGENCE THR O UGH DA TA MINING 5.4 Application level: the training framework The basic functionality of an agent prediction system The knowledge evaluation mechanism The training/retraining mechanism Launching Data Miner a Defining the ontology b Specifying the input file containing the training dataset Preprocessing data Selecting the proper DM technique and algorithm Tuning the selected algorithm Specifying training parameters Specifying output options The outcome of the data mining process The functionality of Data Miner The layers of IRF The IRF architectural diagram Installing IRF on top of an existing ERP The Workflow of SPIA RA order splitting policy The GUI of Customer Order Agent The final IPRA Recommendation The ^-Profile mechanism The evolution of an operation cycle Fuzzy variable time values Fuzzy variable frequency values Output variable weight values The main console of the demonstrator The generated agent recommendations Applying the WAVP metric on the extracted clusters An overview of the Biotope environment Agent vision field and the corresponding vision vector Deciding on the next move, based on the classifier set The possible paths towards the destination cell Establishing communication between neighboring agents Transforming the vision vector into a bit stream Creating new Classifiers 145

9 List of Figures xv 8.8 Configuring environmental parameters Configuring agent parameters Biotope "in action" Comparing E^_3 and EA~ Comparing E^_ 5 and E^_ Comparing EA-7 and EAS Population growth with respect to varying GA application rate Convergence in the behaviors of agent communities when the GA application rate increases The food refresh rate plays a pivotal role in agent survival Retraining the agents of a MAS The O3RTAA system architecture The generalized EMIS architecture Improving the behavior of biding agents A software workflow process A common knowledge repository A semantically-aware architecture 183

10 List of Tables 2.1 Steps in the evolution of Data Mining The Play Golf dataset A sample transaction database The core features of Genetic Algorithms Environment characteristics with respect to agents The basic functionalities of each layer Techniques and algorithms provided by the Data Miner The IRF agent types and their functionality Fuzzification and Interestingness of dataset attributes Service Level and corresponding z Value IPRA inputs and outputs The resulting customer clusters The resulting supplier clusters The generated association rules IRF enhancements to ERPs Recommending the next action An example on predicting the next action A vector representing the operation cycle Mapping agent actions to vectors Fuzzification of input variable time Fuzzification of input variable frequency Fuzzification of output variable weight The resulting vector clusters The actions that comprise the profile of cluster Mapping the contents of Biotope 138

11 xviii AGENT INTELLIGENCE THROUGH DATA MINING 8.2 Perceiving the environment and taking action Agent actions and energy variation rate The application components of Biotope The application menu bar items Fixed parameter values for all the experiments Experiments on agent communication Average indicator values for experiments E^-i to E^_ Average indicator values for experiments E^_5 & E^_ Average indicator values for experiments EA-7 & E^_s Experiments on Genetic Algorithm application Average indicator values for experiments E^-i to Ej5_ Experiments on various environments Average indicator values for experiments Ec-i to Ec_io Retraining options for DIQ. D^Qi Retraining options for DpjQ i DQ Retraining options for DjQ i DQ Retraining options for DjQ i D^Qi DQ { Classification accuracies for the Diagnosis Agent Speech Recognition Agents Classification accuracy The Iris Recommendation Agent success 174

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

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

Detection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup

Detection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup Network Anomaly Detection A Machine Learning Perspective Dhruba Kumar Bhattacharyya Jugal Kumar KaKta»C) CRC Press J Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor

More information

Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group

Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group Practical Data Mining Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor Ei Francis Group, an Informs

More information

Network Machine Learning Research Group. Intended status: Informational October 19, 2015 Expires: April 21, 2016

Network Machine Learning Research Group. Intended status: Informational October 19, 2015 Expires: April 21, 2016 Network Machine Learning Research Group S. Jiang Internet-Draft Huawei Technologies Co., Ltd Intended status: Informational October 19, 2015 Expires: April 21, 2016 Abstract Network Machine Learning draft-jiang-nmlrg-network-machine-learning-00

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

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

Data Mining. Concepts, Models, Methods, and Algorithms. 2nd Edition

Data Mining. Concepts, Models, Methods, and Algorithms. 2nd Edition Brochure More information from http://www.researchandmarkets.com/reports/2171322/ Data Mining. Concepts, Models, Methods, and Algorithms. 2nd Edition Description: This book reviews state-of-the-art methodologies

More information

COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments

COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for

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

DYNAMIC FUZZY PATTERN RECOGNITION WITH APPLICATIONS TO FINANCE AND ENGINEERING LARISA ANGSTENBERGER

DYNAMIC FUZZY PATTERN RECOGNITION WITH APPLICATIONS TO FINANCE AND ENGINEERING LARISA ANGSTENBERGER DYNAMIC FUZZY PATTERN RECOGNITION WITH APPLICATIONS TO FINANCE AND ENGINEERING LARISA ANGSTENBERGER Kluwer Academic Publishers Boston/Dordrecht/London TABLE OF CONTENTS FOREWORD ACKNOWLEDGEMENTS XIX XXI

More information

life science data mining

life science data mining life science data mining - '.)'-. < } ti» (>.:>,u» c ~'editors Stephen Wong Harvard Medical School, USA Chung-Sheng Li /BM Thomas J Watson Research Center World Scientific NEW JERSEY LONDON SINGAPORE.

More information

LIST OF FIGURES. Figure No. Caption Page No.

LIST OF FIGURES. Figure No. Caption Page No. LIST OF FIGURES Figure No. Caption Page No. Figure 1.1 A Cellular Network.. 2 Figure 1.2 A Mobile Ad hoc Network... 2 Figure 1.3 Classifications of Threats. 10 Figure 1.4 Classification of Different QoS

More information

Workflow Administration of Windchill 10.2

Workflow Administration of Windchill 10.2 Workflow Administration of Windchill 10.2 Overview Course Code Course Length TRN-4339-T 2 Days In this course, you will learn about Windchill workflow features and how to design, configure, and test workflow

More information

Comparison of K-means and Backpropagation Data Mining Algorithms

Comparison of K-means and Backpropagation Data Mining Algorithms Comparison of K-means and Backpropagation Data Mining Algorithms Nitu Mathuriya, Dr. Ashish Bansal Abstract Data mining has got more and more mature as a field of basic research in computer science and

More information

A Survey on Intrusion Detection System with Data Mining Techniques

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,

More information

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO)

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) Overview Kyrre Glette kyrrehg@ifi INF3490 Swarm Intelligence Particle Swarm Optimization Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) 3 Swarms in nature Fish, birds,

More information

Clustering Genetic Algorithm

Clustering Genetic Algorithm Clustering Genetic Algorithm Petra Kudová Department of Theoretical Computer Science Institute of Computer Science Academy of Sciences of the Czech Republic ETID 2007 Outline Introduction Clustering Genetic

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

A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM

A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM MS. DIMPI K PATEL Department of Computer Science and Engineering, Hasmukh Goswami college of Engineering, Ahmedabad, Gujarat ABSTRACT The Internet

More information

The Data Warehouse Challenge

The Data Warehouse Challenge The Data Warehouse Challenge Taming Data Chaos Michael H. Brackett Technische Hochschule Darmstadt Fachbereichsbibliothek Informatik TU Darmstadt FACHBEREICH INFORMATIK B I B L I O T H E K Irwentar-Nr.:...H.3...:T...G3.ty..2iL..

More information

Master's projects at ITMO University. Daniil Chivilikhin PhD Student @ ITMO University

Master's projects at ITMO University. Daniil Chivilikhin PhD Student @ ITMO University Master's projects at ITMO University Daniil Chivilikhin PhD Student @ ITMO University General information Guidance from our lab's researchers Publishable results 2 Research areas Research at ITMO Evolutionary

More information

Predicting the Risk of Heart Attacks using Neural Network and Decision Tree

Predicting the Risk of Heart Attacks using Neural Network and Decision Tree Predicting the Risk of Heart Attacks using Neural Network and Decision Tree S.Florence 1, N.G.Bhuvaneswari Amma 2, G.Annapoorani 3, K.Malathi 4 PG Scholar, Indian Institute of Information Technology, Srirangam,

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

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

Object-Oriented Systems Analysis and Design

Object-Oriented Systems Analysis and Design Object-Oriented Systems Analysis and Design Noushin Ashrafi Professor of Information System University of Massachusetts-Boston Hessam Ashrafi Software Architect Pearson Education International CONTENTS

More information

Intrusion Detection. Jeffrey J.P. Tsai. Imperial College Press. A Machine Learning Approach. Zhenwei Yu. University of Illinois, Chicago, USA

Intrusion Detection. Jeffrey J.P. Tsai. Imperial College Press. A Machine Learning Approach. Zhenwei Yu. University of Illinois, Chicago, USA SERIES IN ELECTRICAL AND COMPUTER ENGINEERING Intrusion Detection A Machine Learning Approach Zhenwei Yu University of Illinois, Chicago, USA Jeffrey J.P. Tsai Asia University, University of Illinois,

More information

D A T A M I N I N G C L A S S I F I C A T I O N

D A T A M I N I N G C L A S S I F I C A T I O N D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.

More information

DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES

DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES Vijayalakshmi Mahanra Rao 1, Yashwant Prasad Singh 2 Multimedia University, Cyberjaya, MALAYSIA 1 lakshmi.mahanra@gmail.com

More information

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours.

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours. (International Program) 01219141 Object-Oriented Modeling and Programming 3 (3-0) Object concepts, object-oriented design and analysis, object-oriented analysis relating to developing conceptual models

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

Network Intrusion Detection and Prevention

Network Intrusion Detection and Prevention Ali A. Ghorbani Wei Lu Mahbod Tavallaee Network Intrusion Detection and Prevention Concepts and Techniques )Spri inger Contents 1 Network Attacks 1 1.1 Attack Taxonomies 2 1.2 Probes 4 1.2.1 IPSweep and

More information

Cloud Computing. and Scheduling. Data-Intensive Computing. Frederic Magoules, Jie Pan, and Fei Teng SILKQH. CRC Press. Taylor & Francis Group

Cloud Computing. and Scheduling. Data-Intensive Computing. Frederic Magoules, Jie Pan, and Fei Teng SILKQH. CRC Press. Taylor & Francis Group Cloud Computing Data-Intensive Computing and Scheduling Frederic Magoules, Jie Pan, and Fei Teng SILKQH CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor

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

Scheduler Job Scheduling Console

Scheduler Job Scheduling Console Tivoli IBM Tivoli Workload Scheduler Job Scheduling Console Feature Level 1.3 (Revised December 2004) User s Guide SC32-1257-02 Tivoli IBM Tivoli Workload Scheduler Job Scheduling Console Feature Level

More information

AppFabric. Pro Windows Server. Stephen Kaufman. Danny Garber. Apress. INFORMATIONSBIBLIOTHbK TECHNISCHE. U N! V En SIT AT S R!

AppFabric. Pro Windows Server. Stephen Kaufman. Danny Garber. Apress. INFORMATIONSBIBLIOTHbK TECHNISCHE. U N! V En SIT AT S R! Pro Windows Server AppFabric Stephen Kaufman Danny Garber Apress TECHNISCHE INFORMATIONSBIBLIOTHbK T1B/UB Hannover 133 294 706 U N! V En SIT AT S R! B L' OT H E K HANNOVER Contents it Contents at a Glance

More information

Measuring Data Quality for Ongoing Improvement

Measuring Data Quality for Ongoing Improvement Measuring Data Quality for Ongoing Improvement A Data Quality Assessment Framework Laura Sebastian-Coleman ELSEVIER AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE

More information

Subject Description Form

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

More information

Business Architecture

Business Architecture Business Architecture A Practical Guide JONATHAN WHELAN and GRAHAM MEADEN GOWER Contents List of Figures List of Tables About the Authors Foreword Preface Acknowledgemen ts Abbreviations IX xi xiii xv

More information

International Journal of Software and Web Sciences (IJSWS) Web Log Mining Based on Improved FCM Algorithm using Multiobjective

International Journal of Software and Web Sciences (IJSWS)  Web Log Mining Based on Improved FCM Algorithm using Multiobjective International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International

More information

The Scientific Data Mining Process

The Scientific Data Mining Process Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

More information

Research-based Learning (RbL) in Computing Courses for Senior Engineering Students

Research-based Learning (RbL) in Computing Courses for Senior Engineering Students Research-based Learning (RbL) in Computing Courses for Senior Engineering Students Khaled Bashir Shaban, and Mahmoud Abdulwahed Computer Science and Engineering Department; and CRU, Dean s Office Best

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 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

Feature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier

Feature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier Feature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier D.Nithya a, *, V.Suganya b,1, R.Saranya Irudaya Mary c,1 Abstract - This paper presents,

More information

BSM 9.0 ESSENTIALS. Instructor-Led Training

BSM 9.0 ESSENTIALS. Instructor-Led Training BSM 9.0 ESSENTIALS Instructor-Led Training INTENDED AUDIENCE New users of Business Service Management (BSM) 9.0, including: Database Administrators System Administrators Network Administrators Operations

More information

ARIS Design Platform Getting Started with BPM

ARIS Design Platform Getting Started with BPM Rob Davis and Eric Brabander ARIS Design Platform Getting Started with BPM 4y Springer Contents Acknowledgements Foreword xvii xix Chapter 1 An Introduction to BPM 1 1.1 Brief History of Business Process

More information

Data Mining Using Neural Networks

Data Mining Using Neural Networks Data Mining Using Neural Networks A thesis Submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy S. M. Monzurur Rahman B.Sc.Eng., M.App.Sc. School of Electrical and Computer

More information

Comparison and Analysis of Various Clustering Methods in Data mining On Education data set Using the weak tool

Comparison and Analysis of Various Clustering Methods in Data mining On Education data set Using the weak tool Comparison and Analysis of Various Clustering Metho in Data mining On Education data set Using the weak tool Abstract:- Data mining is used to find the hidden information pattern and relationship between

More information

Clustering. Data Mining. Abraham Otero. Data Mining. Agenda

Clustering. Data Mining. Abraham Otero. Data Mining. Agenda Clustering 1/46 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference 2/46 1 Introduction It seems logical that in a new situation we should act in a similar way as in

More information

MULTI AGENT-BASED DISTRIBUTED DATA MINING

MULTI AGENT-BASED DISTRIBUTED DATA MINING MULTI AGENT-BASED DISTRIBUTED DATA MINING REECHA B. PRAJAPATI 1, SUMITRA MENARIA 2 Department of Computer Science and Engineering, Parul Institute of Technology, Gujarat Technology University Abstract:

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

DATA MINING IN FINANCE

DATA MINING IN FINANCE DATA MINING IN FINANCE Advances in Relational and Hybrid Methods by BORIS KOVALERCHUK Central Washington University, USA and EVGENII VITYAEV Institute of Mathematics Russian Academy of Sciences, Russia

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

Data Mining. Vera Goebel. Department of Informatics, University of Oslo

Data Mining. Vera Goebel. Department of Informatics, University of Oslo Data Mining Vera Goebel Department of Informatics, University of Oslo 2011 1 Lecture Contents Knowledge Discovery in Databases (KDD) Definition and Applications OLAP Architectures for OLAP and KDD KDD

More information

Contents. Biography. Acknowledgments. List of Abbreviations. List of Symbols

Contents. Biography. Acknowledgments. List of Abbreviations. List of Symbols Contents Biography Preface Acknowledgments List of Abbreviations List of Symbols xi xiii xvii xix xxvii 1 Introduction 1 1.1 Cellular Mobile Communication Systems 1 1.1.1 The Cellular Concept 2 1.1.2 Propagation

More information

Contents. Introduction and System Engineering 1. Introduction 2. Software Process and Methodology 16. System Engineering 53

Contents. Introduction and System Engineering 1. Introduction 2. Software Process and Methodology 16. System Engineering 53 Preface xvi Part I Introduction and System Engineering 1 Chapter 1 Introduction 2 1.1 What Is Software Engineering? 2 1.2 Why Software Engineering? 3 1.3 Software Life-Cycle Activities 4 1.3.1 Software

More information

Developing. and Securing. the Cloud. Bhavani Thuraisingham CRC. Press. Taylor & Francis Group. Taylor & Francis Croup, an Informs business

Developing. and Securing. the Cloud. Bhavani Thuraisingham CRC. Press. Taylor & Francis Group. Taylor & Francis Croup, an Informs business Developing and Securing the Cloud Bhavani Thuraisingham @ CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Croup, an Informs business AN AUERBACH

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

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

Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs

Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs 1.1 Introduction Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs For brevity, the Lavastorm Analytics Library (LAL) Predictive and Statistical Analytics Node Pack will be

More information

Knowledge Discovery in Data with FIT-Miner

Knowledge Discovery in Data with FIT-Miner Knowledge Discovery in Data with FIT-Miner Michal Šebek, Martin Hlosta and Jaroslav Zendulka Faculty of Information Technology, Brno University of Technology, Božetěchova 2, Brno {isebek,ihlosta,zendulka}@fit.vutbr.cz

More information

A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery

A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery Alex A. Freitas Postgraduate Program in Computer Science, Pontificia Universidade Catolica do Parana Rua Imaculada Conceicao,

More information

Contents. Foreword. Acknowledgments

Contents. Foreword. Acknowledgments Foreword Preface Acknowledgments xv xvii xviii CHAPTER 1 Introduction 1 1.1 What Is Mission Critical? 1 1.2 Purpose of the Book 2 1.3 Network Continuity Versus Disaster Recovery 2 1.4 The Case for Mission-Critical

More information

Manjeet Kaur Bhullar, Kiranbir Kaur Department of CSE, GNDU, Amritsar, Punjab, India

Manjeet Kaur Bhullar, Kiranbir Kaur Department of CSE, GNDU, Amritsar, Punjab, India Volume 5, Issue 6, June 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Multiple Pheromone

More information

Business Administration of Windchill PDMLink 10.0

Business Administration of Windchill PDMLink 10.0 Business Administration of Windchill PDMLink 10.0 Overview Course Code Course Length TRN-3160-T 3 Days After completing this course, you will be well prepared to set up and manage a basic Windchill PDMLink

More information

SOFTWARE TESTING AS A SERVICE

SOFTWARE TESTING AS A SERVICE SOFTWARE TESTING AS A SERVICE ASHFAQUE AHMED (g) CRC Press Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business AN AUERBACH BOOK

More information

TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM

TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM Thanh-Nghi Do College of Information Technology, Cantho University 1 Ly Tu Trong Street, Ninh Kieu District Cantho City, Vietnam

More information

Data Algorithms. Mahmoud Parsian. Tokyo O'REILLY. Beijing. Boston Farnham Sebastopol

Data Algorithms. Mahmoud Parsian. Tokyo O'REILLY. Beijing. Boston Farnham Sebastopol Data Algorithms Mahmoud Parsian Beijing Boston Farnham Sebastopol Tokyo O'REILLY Table of Contents Foreword xix Preface xxi 1. Secondary Sort: Introduction 1 Solutions to the Secondary Sort Problem 3 Implementation

More information

Professor, D.Sc. (Tech.) Eugene Kovshov MSTU «STANKIN», Moscow, Russia

Professor, D.Sc. (Tech.) Eugene Kovshov MSTU «STANKIN», Moscow, Russia Professor, D.Sc. (Tech.) Eugene Kovshov MSTU «STANKIN», Moscow, Russia As of today, the issue of Big Data processing is still of high importance. Data flow is increasingly growing. Processing methods

More information

Predictive Dynamix Inc

Predictive Dynamix Inc Predictive Modeling Technology Predictive modeling is concerned with analyzing patterns and trends in historical and operational data in order to transform data into actionable decisions. This is accomplished

More information

Table of Contents. Chapter No. 1 Introduction 1. iii. xiv. xviii. xix. Page No.

Table of Contents. Chapter No. 1 Introduction 1. iii. xiv. xviii. xix. Page No. Table of Contents Title Declaration by the Candidate Certificate of Supervisor Acknowledgement Abstract List of Figures List of Tables List of Abbreviations Chapter Chapter No. 1 Introduction 1 ii iii

More information

Engineering Design. Software. Theory and Practice. Carlos E. Otero. CRC Press. Taylor & Francis Croup. Taylor St Francis Croup, an Informa business

Engineering Design. Software. Theory and Practice. Carlos E. Otero. CRC Press. Taylor & Francis Croup. Taylor St Francis Croup, an Informa business Software Engineering Design Theory and Practice Carlos E. Otero CRC Press Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor St Francis Croup, an Informa business AN

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

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

Quality Assessment in Spatial Clustering of Data Mining

Quality Assessment in Spatial Clustering of Data Mining Quality Assessment in Spatial Clustering of Data Mining Azimi, A. and M.R. Delavar Centre of Excellence in Geomatics Engineering and Disaster Management, Dept. of Surveying and Geomatics Engineering, Engineering

More information

Explorer's Guide to the Semantic Web

Explorer's Guide to the Semantic Web Explorer's Guide to the Semantic Web THOMAS B. PASSIN 11 MANNING Greenwich (74 w. long.) contents preface xiii acknowledgments xv about this booh xvii The Semantic Web 1 1.1 What is the Semantic Web? 3

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015

International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015 RESEARCH ARTICLE OPEN ACCESS Data Mining Technology for Efficient Network Security Management Ankit Naik [1], S.W. Ahmad [2] Student [1], Assistant Professor [2] Department of Computer Science and Engineering

More information

lop Building Machine Learning Systems with Python en source

lop Building Machine Learning Systems with Python en source Building Machine Learning Systems with Python Master the art of machine learning with Python and build effective machine learning systems with this intensive handson guide Willi Richert Luis Pedro Coelho

More information

A Case of Study on Hadoop Benchmark Behavior Modeling Using ALOJA-ML

A Case of Study on Hadoop Benchmark Behavior Modeling Using ALOJA-ML www.bsc.es A Case of Study on Hadoop Benchmark Behavior Modeling Using ALOJA-ML Josep Ll. Berral, Nicolas Poggi, David Carrera Workshop on Big Data Benchmarks Toronto, Canada 2015 1 Context ALOJA: framework

More information

1. Classification problems

1. Classification problems Neural and Evolutionary Computing. Lab 1: Classification problems Machine Learning test data repository Weka data mining platform Introduction Scilab 1. Classification problems The main aim of a classification

More information

Data Mining and Neural Networks in Stata

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

More information

Master Thesis. Defining patterns in unstructured manifests in a volatile cross-domain environment. Prof. Dr. M. Biehl. Drs. A.

Master Thesis. Defining patterns in unstructured manifests in a volatile cross-domain environment. Prof. Dr. M. Biehl. Drs. A. Defining patterns in unstructured manifests in a volatile cross-domain environment Master Thesis Supervisors for university of Groningen: Supervisors for Logica: Author: Prof. Dr. M. Aiello Prof. Dr. M.

More information

Grid Density Clustering Algorithm

Grid Density Clustering Algorithm Grid Density Clustering Algorithm Amandeep Kaur Mann 1, Navneet Kaur 2, Scholar, M.Tech (CSE), RIMT, Mandi Gobindgarh, Punjab, India 1 Assistant Professor (CSE), RIMT, Mandi Gobindgarh, Punjab, India 2

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

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

Malay A. Dalal Madhav Erraguntla Perakath Benjamin. Knowledge Based Systems, Inc. (KBSI) College Station, TX 77840, U.S.A.

Malay A. Dalal Madhav Erraguntla Perakath Benjamin. Knowledge Based Systems, Inc. (KBSI) College Station, TX 77840, U.S.A. AN INTRODUCTION TO USING PROSIM FOR BUSINESS PROCESS SIMULATION AND ANALYSIS Malay A. Dalal Madhav Erraguntla Perakath Benjamin Knowledge Based Systems, Inc. (KBSI) College Station, TX 77840, U.S.A. ABSTRACT

More information

Advances in Network Management

Advances in Network Management Advances in Network Management Jianguo Ding UC) CRC Press >5^ J Taylor & Francis Croup ^""""^ Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business AN AUERBACH

More information

A Review of Anomaly Detection Techniques in Network Intrusion Detection System

A Review of Anomaly Detection Techniques in Network Intrusion Detection System A Review of Anomaly Detection Techniques in Network Intrusion Detection System Dr.D.V.S.S.Subrahmanyam Professor, Dept. of CSE, Sreyas Institute of Engineering & Technology, Hyderabad, India ABSTRACT:In

More information

A survey on Data Mining based Intrusion Detection Systems

A survey on Data Mining based Intrusion Detection Systems International Journal of Computer Networks and Communications Security VOL. 2, NO. 12, DECEMBER 2014, 485 490 Available online at: www.ijcncs.org ISSN 2308-9830 A survey on Data Mining based Intrusion

More information

dm106 TEXT MINING FOR CUSTOMER RELATIONSHIP MANAGEMENT: AN APPROACH BASED ON LATENT SEMANTIC ANALYSIS AND FUZZY CLUSTERING

dm106 TEXT MINING FOR CUSTOMER RELATIONSHIP MANAGEMENT: AN APPROACH BASED ON LATENT SEMANTIC ANALYSIS AND FUZZY CLUSTERING dm106 TEXT MINING FOR CUSTOMER RELATIONSHIP MANAGEMENT: AN APPROACH BASED ON LATENT SEMANTIC ANALYSIS AND FUZZY CLUSTERING ABSTRACT In most CRM (Customer Relationship Management) systems, information on

More information

Knowledge Based Descriptive Neural Networks

Knowledge Based Descriptive Neural Networks Knowledge Based Descriptive Neural Networks J. T. Yao Department of Computer Science, University or Regina Regina, Saskachewan, CANADA S4S 0A2 Email: jtyao@cs.uregina.ca Abstract This paper presents a

More information

Business Intelligence. Data Mining and Optimization for Decision Making

Business Intelligence. Data Mining and Optimization for Decision Making Brochure More information from http://www.researchandmarkets.com/reports/2325743/ Business Intelligence. Data Mining and Optimization for Decision Making Description: Business intelligence is a broad category

More information

Mercy Health System. St. Louis, MO. Process Mining of Clinical Workflows for Quality and Process Improvement

Mercy Health System. St. Louis, MO. Process Mining of Clinical Workflows for Quality and Process Improvement Mercy Health System St. Louis, MO Process Mining of Clinical Workflows for Quality and Process Improvement Paul Helmering, Executive Director, Enterprise Architecture Pete Harrison, Data Analyst, Mercy

More information

Quality Management. Theory and Application PETER D. MAUCH. Ltfi) CRC Press. \ V J Taylor & Francis Group. ^ ^ Boca Raton London New York

Quality Management. Theory and Application PETER D. MAUCH. Ltfi) CRC Press. \ V J Taylor & Francis Group. ^ ^ Boca Raton London New York Quality Management Theory and Application PETER D. MAUCH Ltfi) CRC Press \ V J Taylor & Francis Group ^ ^ Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an Informa business

More information

MOBILE VIDEO WITH MOBILE IPv6

MOBILE VIDEO WITH MOBILE IPv6 MOBILE VIDEO WITH MOBILE IPv6 DANIEL MINOLI WILEY A JOHN WILEY & SONS, INC., PUBLICATION CONTENTS PREFACE ABOUT THE AUTHOR xi xiii 1 THE MOBILE USER ENVIRONMENT: SMART PHONES, PORTABLE MEDIA PLAYERS (PMPs),

More information

An Enhanced Clustering Algorithm to Analyze Spatial Data

An Enhanced Clustering Algorithm to Analyze Spatial Data International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-7, July 2014 An Enhanced Clustering Algorithm to Analyze Spatial Data Dr. Mahesh Kumar, Mr. Sachin Yadav

More information

The Masters of Science in Information Systems & Technology

The Masters of Science in Information Systems & Technology The Masters of Science in Information Systems & Technology College of Engineering and Computer Science University of Michigan-Dearborn A Rackham School of Graduate Studies Program PH: 313-593-5361; FAX:

More information

ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION

ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION ISSN 9 X INFORMATION TECHNOLOGY AND CONTROL, 00, Vol., No.A ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION Danuta Zakrzewska Institute of Computer Science, Technical

More information

Big Data JAMES WARREN. Principles and best practices of NATHAN MARZ MANNING. scalable real-time data systems. Shelter Island

Big Data JAMES WARREN. Principles and best practices of NATHAN MARZ MANNING. scalable real-time data systems. Shelter Island Big Data Principles and best practices of scalable real-time data systems NATHAN MARZ JAMES WARREN II MANNING Shelter Island contents preface xiii acknowledgments xv about this book xviii ~1 Anew paradigm

More information