Advanced Data Mining Techniques David L. Olson · Dursun Delen Advanced Data Mining Techniques Dr. Dursun Delen Department of Management Science Department of Management University of Nebraska Science and Information Systems Lincoln, NE 68588-0491 700 North Greenwood Avenue USA Tulsa, Oklahoma 74106 dolson3@unl.edu USA dursun.edu ISBN: 978-3-540-76916-3 e-ISBN: 978-3-540-76917-0 Library of Congress Control Number: 2007940052 c 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks.
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Cover design: WMX Design, Heidelberg Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com I dedicate this book to my grandchildren. Olson I dedicate this book to my children, Altug and Serra. Dursun Delen Preface The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve uncer- tain description or other complexities that cause difficulty for the conven- tional approaches of logistic regression, neural network models, and deci- sion trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex.
We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Book Concept Our intent is to cover the fundamental concepts of data mining, to demon- strate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts.
Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice.
A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.com will be provided. Part I: Introduction Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided.
Chapter 2 presents the data mining process in more detail. It demonstrates this process with a typical set of data. Visualization of data through data mining software is addressed. VIII Preface Part II: Data Mining Methods as Tools Chapter 3 presents memory-based reasoning methods of data mining.
Major real applications are described. Algorithms are demonstrated with prototypical data based on real applications. Chapter 4 discusses association rule methods. Application in the form of market basket analysis is discussed.
A real data set is described, and a sim- plified version used to demonstrate association rule methods. Chapter 5 presents fuzzy data mining approaches. Fuzzy decision tree ap- proaches are described, as well as fuzzy association rule applications. Real data mining applications are described and demonstrated Chapter 6 presents Rough Sets, a recently popularized data mining method.
Chapter 7 describes support vector machines and the types of data sets in which they seem to have relative advantage. Chapter 8 discusses the use of genetic algorithms to supplement various data mining operations. Chapter 9 describes methods to evaluate models in the process of data mining. Part III: Applications Chapter 10 presents a spectrum of successful applications of the data min- ing techniques, focusing on the value of these analyses to business deci- sion making.
University of Nebraska-Lincoln David L. Olson Oklahoma State University Dursun Delen Contents Part I INTRODUCTION 1 Introduction.3 What is Data Mining? .5 What is Needed to Do Data Mining.5 Business Data Mining.7 Data Mining Tools .8 2 Data Mining Process. 19 Steps in SEMMA Process. 20 Example Data Mining Process Application.
22 Comparison of CRISP & SEMMA. 34 Part II DATA MINING METHODS AS TOOLS 3 Memory-Based Reasoning Methods. 50 Appendix: Job Application Data Set. 51 X Contents 4 Association Rules in Knowledge Discovery.
53 Market-Basket Analysis. 55 Market Basket Analysis Benefits. 56 Demonstration on Small Set of Data. 57 Real Market Basket Data.
59 The Counting Method Without Software. 68 5 Fuzzy Sets in Data Mining. 69 Fuzzy Sets and Decision Trees. 71 Fuzzy Sets and Ordinal Classification.
75 Fuzzy Association Rules. 87 A Brief Theory of Rough Sets. 89 Some Exemplary Applications of Rough Sets. 91 Rough Sets Software Tools.
93 The Process of Conducting Rough Sets Analysis. 93 1 Data Pre-Processing. 97 5 Rule Generation and Rule Filtering. 99 6 Apply the Discretization Cuts to Test Dataset.
100 7 Score the Test Dataset on Generated Rule set (and measuring the prediction accuracy). 100 8 Deploying the Rules in a Production System. 109 7 Support Vector Machines. 111 Formal Explanation of SVM.
114 Contents XI Dual Form. 114 Non-linear Classification. 117 Use of SVM – A Process-Based Approach. 118 Support Vector Machines versus Artificial Neural Networks.
121 Disadvantages of Support Vector Machines. 122 8 Genetic Algorithm Support to Data Mining. 125 Demonstration of Genetic Algorithm. 126 Application of Genetic Algorithms in Data Mining.
132 Appendix: Loan Application Data Set. 133 9 Performance Evaluation for Predictive Modeling. 137 Performance Metrics for Predictive Modeling. 137 Estimation Methodology for Classification Models.
140 The k-Fold Cross Validation. 141 Bootstrapping and Jackknifing. 143 Area Under the ROC Curve. 147 Part III APPLICATIONS 10 Applications of Methods.
151 Memory-Based Application. 151 Association Rule Application. 153 Fuzzy Data Mining. 155 Rough Set Models.
155 Support Vector Machine Application. 157 Genetic Algorithm Applications. 158 Japanese Credit Screening. 158 Product Quality Testing Design.
160 XII Contents Predicting the Financial Success of Hollywood Movies. 162 Problem and Data Description. 163 Comparative Analysis of the Data Mining Methods. 177 Part I INTRODUCTION 1 Introduction Data mining refers to the analysis of the large quantities of data that are stored in computers.
For example, grocery stores have large amounts of data generated by our purchases. Bar coding has made checkout very con- venient for us, and provides retail establishments with masses of data. Gro- cery stores and other retail stores are able to quickly process our purchases, and use computers to accurately determine product prices. These same com- puters can help the stores with their inventory management, by instantane- ously determining the quantity of items of each product on hand.
They are also able to apply computer technology to contact their vendors so that they do not run out of the things that we want to purchase. Computers allow the store’s accounting system to more accurately measure costs, and determine the profit that store stockholders are concerned about. All of this information is available based upon the bar coding information attached to each product. Along with many other sources of information, information gathered through bar coding can be used for data mining analysis.
Data mining is not limited to business. Both major parties in the 2004 U. election utilized data mining of potential voters.1 Data mining has been heavily used in the medical field, to include diagnosis of patient re- cords to help identify best practices.2 The Mayo Clinic worked with IBM to develop an online computer system to identify how that last 100 Mayo patients with the same gender, age, and medical history had responded to particular treatments.3 Data mining is widely used by banking firms in soliciting credit card customers,4 by insurance and telecommunication companies in detecting 1 H. IT efforts to help determine election successes, failures: Dems deploy data tools; GOP expands microtargeting use, Computerworld 40: 45, 11 Sep 2006, 1, 16.
Expect increased adoption rates of certain types of EHRs, EMRs, Managed Healthcare Executive 16:4, 58. IBM, Mayo clinic to mine medical data, The Information Management Journal 38:6, Nov/Dec 2004, 8. The study and veri- fication of mathematical modeling for customer purchasing behavior, Journal of Computer Information Systems 47:2, 46–57. 4 1 Introduction fraud,5 by telephone companies and credit card issuers in identifying those potential customers most likely to churn,6 by manufacturing firms in qual- ity control,7 and many other applications.
Data mining is being applied to improve food and drug product safety,8 and detection of terrorists or crimi- nals.9 Data mining involves statistical and/or artificial intelligence analysis, usually applied to large-scale data sets. Traditional statistical analysis in- volves an approach that is usually directed, in that a specific set of ex- pected outcomes exists. This approach is referred to as supervised (hy- pothesis development and testing). However, there is more to data mining than the technical tools used.
Data mining involves a spirit of knowledge discovery (learning new and useful things). Knowledge discovery is re- ferred to as unsupervised (knowledge discovery) Much of this can be ac- complished through automatic means, as we will see in decision tree analysis, for example. But data mining is not limited to automated analy- sis. Knowledge discovery by humans can be enhanced by graphical tools and identification of unexpected patterns through a combination of human and computer interaction.
Data mining can be used by businesses in many ways. Three examples are: 1. Customer profiling, identifying those subsets of customers most profitable to the business; 2. Targeting, determining the characteristics of profitable customers who have been captured by competitors; 3.
Market-basket analysis, determining product purchases by consumer, which can be used for product positioning and for cross-selling. These are not the only applications of data mining, but are three important applications useful to businesses. Using data mining to detect crop insurance fraud: Is there a role for social scientists? Journal of Financial Crime 12:1, 24–32. Survival data mining for customer insight, Intelligent Enter- prise 7:12, 28–33.
Data mining for improvement of product quality, International Journal of Production Research 44:18/19, 4041–4054. Drug safety, the U. Food and Drug Administration and statistical data mining, Scientific Computing 23:7, 32–33., Data mining: Early attention to privacy in developing a key DHS program could reduce risks, GAO Report 07-293, 3/21/2007. What is Needed to Do Data Mining 5 What is Data Mining? Data mining has been called exploratory data analysis, among other things.
Masses of data generated from cash registers, from scanning, from topic- specific databases throughout the company, are explored, analyzed, reduced, and reused. Searches are performed across different models proposed for predicting sales, marketing response, and profit. Classical statistical ap- proaches are fundamental to data mining. Automated AI methods are also used.
However, systematic exploration through classical statistical meth- ods is still the basis of data mining. Some of the tools developed by the field of statistical analysis are harnessed through automatic control (with some key human guidance) in dealing with data. A variety of analytic computer models have been used in data mining. The standard model types in data mining include regression (normal re- gression for prediction, logistic regression for classification), neural net- works, and decision trees.
These techniques are well known. This book fo- cuses on less used techniques applied to specific problem types, to include association rules for initial data exploration, fuzzy data mining approaches, rough set models, support vector machines, and genetic algorithms. The book will also review some interesting applications in business, and con- clude with a comparison of methods.
