Data Mining Methods and Models Review

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(More customer reviews)This book is part of a Data Mining three book series. The first book is the strongest of the three, and should be considered first. My advice on this book is that if you 1) have a dozen or more related books, and 2) you have already worked on at least one or two data mining projects, you should buy this book. It has enough good material to add it to your collection. If you are new to data mining you should look elsewhere.
The other two books are: Discovering Knowledge in Data: An Introduction to Data Mining and Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage.
Methods and Models can be divided into three parts. The first part is a very strong 200 page review of traditional statistical techniques, specifically PCA/Factor Analysis, Multiple Regression, and Logistic Regression. These techniques absolutely belong in the Data Miners toolkit, but on most projects won't be as important as Decision Trees (covered in the first book). However, these are classical techniques, and the typical reader may have a lot of training or/and books on this material already. Having said that, this material is extremely well written. So if you are looking for a great review of these techniques you can do much worse. If you are looking for a discussion of how the application of these techniques to data mining differs from their application to statistics, you will be disappointed.
The second part of the book is the most helpful to me. It comprises of one chapter on Bayesian Networks and another on Genetic Algorithms. As always, the writing is clear, and to owners of SPSS Clementine 12.0, this section is of special interest because Bayesian Networks have just been added to Clementine. Methods and Models pre-dates the release of Clem 12.0, so it does not refer to it.
The third part of the book is a 50 page case study using the CRISP-DM methodology. This is a welcome addition, but is flawed in its execution. There are moments of brilliance: an application of over-balancing, a voting model, and tables showing several variations of models with their accuracies. Then ... the inevitable appearance in this book series of linear transformations and factor analysis as precursors to techniques that either do them automatically in Clementine, or are unneeded altogether. The veteran will know when to ignore this book, and when to pay attention- the novice might not.
In short, you should consider the first book, or both as a pair to add to a veteran data miner's collection. Only the rare reader should buy this book alone, and never as their first and only data mining book.

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Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable ResultsData Mining Methods and Models provides:* The latest techniques for uncovering hidden nuggets of information* The insight into how the data mining algorithms actually work* The hands-on experience of performing data mining on large data setsData Mining Methods and Models:* Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing"* Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises* Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software* Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes.With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field.An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.

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Learning in Graphical Models (NATO Science Series D: (closed)) Review

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(More customer reviews)The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it.
To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction.
What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.

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In the past decade, a number of different research communitieswithin the computational sciences have studied learning in networks,starting from a number of different points of view. There has beensubstantial progress in these different communities and surprisingconvergence has developed between the formalisms. The awareness ofthis convergence and the growing interest of researchers inunderstanding the essential unity of the subject underlies the currentvolume. Two research communities which have used graphical or networkformalisms to particular advantage are the belief networkcommunity and the neural network community. Belief networksarose within computer science and statistics and were developed withan emphasis on prior knowledge and exact probabilistic calculations.Neural networks arose within electrical engineering, physics andneuroscience and have emphasised pattern recognition and systemsmodelling problems. This volume draws together researchers from thesetwo communities and presents both kinds of networks as instances of ageneral unified graphical formalism. The book focuses on probabilisticmethods for learning and inference in graphical models, algorithmanalysis and design, theory and applications. Exact methods, samplingmethods and variational methods are discussed in detail. Audience: A wide cross-section of computationally orientedresearchers, including computer scientists, statisticians, electricalengineers, physicists and neuroscientists.

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Information Retrieval: Algorithms and Heuristics (The Springer International Series in Engineering and Computer Science) Review

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(More customer reviews)If you're working in the IR industry, or want to develop software in this field, this book is a great starting point. A clarification: this will is not a book for researchers -- instead think of it as a book for advanced practitioners or engineers needing to work in this area. Inside you'll see complete worked examples of several fundamental computations rather than detailed proofs.

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Information Retrieval: Algorithms and Heuristics is acomprehensive introduction to the study of information retrievalcovering both effectiveness and run-time performance. The focus of thepresentation is on algorithms and heuristics used to find documentsrelevant to the user request and to find them fast. Through multipleexamples, the most commonly used algorithms and heuristics needed aretackled. To facilitate understanding and applications, introductionsto and discussions of computational linguistics, natural languageprocessing, probability theory and library and computer science areprovided. While this text focuses on algorithms and not on commercialproduct per se, the basic strategies used by many commercial productsare described. Techniques that can be used to find information on theWeb, as well as in other large information collections, are included.This volume is an invaluable resource for researchers, practitioners,and students working in information retrieval and databases. Forinstructors, a set of Powerpoint slides, including speaker notes, areavailable online from the authors.

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The New Direct Marketing: How to Implement A Profit-Driven Database Marketing Strategy Review

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(More customer reviews)This will be my reference book in this subject matter. It covers a lot of detail I'm convinced the authors have been collecting these nuggets since the first day they started working then poured it all out into this book. This is good and bad. It makes for a not-so-easy read. Some chapters almost just ramble this way and that. Wouldn't you rather a book err on too much detail than too little, though? I was very pleased though, that the book has many step-by-step how-tos. For example, what to include in a Functional Requirements doc, what to include in a system implmentation design doc. Fifteen steps of putting a Marketing Contact Program in place. This book is not an easy read. The chapters are written by different authors so you get a variety of styles, depth, rambling-ness. If you ever have the opportunity to hear THE David Shepard speak, don't miss it. I heard him give a 3-hour session at NCDM in Orlando this month. He is funny, engaging, delivers clearly and articulately and I learned a lot!

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Data Mining Techniques in CRM: Inside Customer Segmentation Review

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(More customer reviews)I recently finished reading Data Mining Techniques in CRM: Inside Customer Segmentation. It is a very didactic book written by Tsiptsis and Chorianopoulos. The authors did a very good job in vulgarizing data mining concepts for the reader. That means nearly no formula. But don't misunderstand me, this is not a book only for beginner. Some deep concepts of data mining are presented. The only particularity is that everything is explained with words and pictures. I really appreciated the approach taken by the authors.
The first part of the book explains data mining concepts. Techniques such as clustering, PCA (Principal Component Analysis) and decision trees are introduced. Since clustering is the most used technique in CRM (Customer Relationship Management), it has a particular focus from the authors. Specific topics such as evaluating the clustering results or profiling are discussed. A very interesting chapter is the one showing examples of data marts in CRM applications (retailers, telco and retail banking).
The second part of the book focuses on CRM applications such as segmentation, cross/up-selling, churn, etc. Full chapters are devoted to customer segmentation in banking, retail and telco. These chapters really give detailed information for such projects (data to consider, aggregations, important factors, result interpretation, etc.). It is clear that the authors have a strong experience in CRM.
To conclude, this is an excellent book for any data miner or anybody involved in CRM. The text is clear and pictures are well done (and funny which is rare enough to be mentioned). From basic to advanced topics, the book is a very pleasant journey inside data mining with a clear focus on customer segmentation. Really advised if you're not a fan of formulas.

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Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring (Wiley and SAS Business Series) Review

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(More customer reviews)I am a model expert in credit risk and it happened that I was looking for a text book that offers different approaches in calculating the score band. Offcourse, based on the great review and on the SAS icon on the back cover it seemed that this book is a good base line.(I was not able to read inside the book and now I know why the publisher didn't add this feature with amazon).It was really a disappionting buy for me, the author was talking about basics that every one in the industry should know??if there is any value of this book is the value of the sas code. AS,a buyer who is looking for a sas code to execute the very generic knowledge of the book,let me tell you that will see literary three small tables of four lines of code in each in the whole book...for practionares don't ever buy this book,for managers I don't know, they are managers and they should kow these basics, for people who knows nothing about the consumer industry then it is a good start.
Usually,I don't list any comments for any book becasue i research the book before the buy, but this book tricked me and it deserves these couple minutes of comment to pay back the publisher...Please don't list this book as a SAS book and remove the SAS icon from the back cover it is embaressing!!

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Praise for Credit Risk Scorecards
"Scorecard development is important to retail financial services in terms of credit risk management, Basel II compliance, and marketing of credit products. Credit Risk Scorecards provides insight into professional practices in different stages of credit scorecard development, such as model building, validation, and implementation. The book should be compulsory reading for modern credit risk managers."—Michael C. S. Wong Associate Professor of Finance, City University of Hong Kong Hong Kong Regional Director, Global Association of Risk Professionals
"Siddiqi offers a practical, step-by-step guide for developing and implementing successful credit scorecards. He relays the key steps in an ordered and simple-to-follow fashion. A 'must read' for anyone managing the development of a scorecard."—Jonathan G. Baum Chief Risk Officer, GE Consumer Finance, Europe
"A comprehensive guide, not only for scorecard specialists but for all consumer credit professionals. The book provides the A-to-Z of scorecard development, implementation, and monitoring processes. This is an important read for all consumer-lending practitioners."—Satinder Ahluwalia Vice President and Head-Retail Credit, Mashreqbank, UAE
"This practical text provides a strong foundation in the technical issues involved in building credit scoring models. This book will become required reading for all those working in this area."—J. Michael Hardin, PhD Professor of StatisticsDepartment of Information Systems, Statistics, and Management ScienceDirector, Institute of Business Intelligence
"Mr. Siddiqi has captured the true essence of the credit risk practitioner's primary tool, the predictive scorecard. He has combined both art and science in demonstrating the critical advantages that scorecards achieve when employed in marketing, acquisition, account management, and recoveries. This text should be part of every risk manager's library."—Stephen D. Morris Director, Credit Risk, ING Bank of Canada

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Handbook of Statistical Analysis and Data Mining Applications Review

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(More customer reviews)The "Handbook of Statistical Analysis & Data Mining Applications" is the finest book I have seen on the subject. It is not only a beautifully crafted book, with numerous color graphs, chart, tables, and screen shots, but the statistical discussion is both clear and comprehensive.
The text does not use only one statistical data mining application to display examples, but provides a rather thorough training in the use of both SAS-Enterprise Miner and STATISTICA Data Miner. A section on SPSS Clementine is also provided, giving comparisons between the various packages. Also employed are STATISTICA's C&RT, CHAID, MARSpline, and other data mining and graphical analytic tools.
The text does not burden the typical data mining researcher with the internals of how the various tools work. It is therefore not steeped in equations. Some are to be found, of course, but the emphasis is on understanding the concepts involved and on how to apply these concepts to real data - which is provided to the reader in terms of data tutorials. Specialized datasets have been prepared by both authors and outside experts in various areas of inquiry ranging from entertainment, financial, engineering, clinical psychology, dentistry, demographics, medical informatics, meteorology, astronomy, and more. Each tutorial is associated with data stored on either the associated CD that comes with the book, or which can be downloaded from a companion web site. Worked out examples of how to use data mining techniques on such data is provided to help the reader gain a solid feel for the data mining enterprise. The final third of the book is devoted to a partial selection of the available tutorials. The two earlier chapters demonstrate how to use data mining software for the analysis of data.
I highly recommend this work to anyone having an interest in data mining. I might also add that the Amazon price of $72.37 is truly excellent for an 864 page academic text, having full color tables and screen shots on some one-third of the pages, plus a CD. A bargain indeed.


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Information Visualization in Data Mining and Knowledge Discovery (The Morgan Kaufmann Series in Data Management Systems) Review

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(More customer reviews)This is very likely the worst book I have ever seen.
Some chapters are barely longer than one (1! and I
am not kidding!) page and merly point to the one
reference, which is - surprise - written by the same
author. There are also chapters that are bit longer
but highly reduandant to other material in the
book - a section on data visualization shows pretty
much the same pictures and graphs than the one on model
visualization. The book does not even attempt to be
consistent or have any flow besides a rough grouping
into a couple of categories.
I find it disturbing that such a bad collection of
obviously non-edited abstracts and papers makes it
into a book. I guess the editors (or publisher?) just
assumed that something with "visualization" and "data
mining" in the title would sell no matter what?

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Mainstream data mining techniques significantly limit the role of human reasoning and insight. Likewise, in data visualization, the role of computational analysis is relatively small. The power demonstrated individually by these approaches to knowledge discovery suggests that somehow uniting the two could lead to increased efficiency and more valuable results. But is this true? How might it be achieved? And what are the consequences for data-dependent enterprises?
Information Visualization in Data Mining and Knowledge Discovery is the first book to ask and answer these thought-provoking questions. It is also the first book to explore the fertile ground of uniting data mining and data visualization principles in a new set of knowledge discovery techniques. Leading researchers from the fields of data mining, data visualization, and statistics present findings organized around topics introduced in two recent international knowledge discovery and data mining workshops. Collected and edited by three of the area's most influential figures, these chapters introduce the concepts and components of visualization, detail current efforts to include visualization and user interaction in data mining, and explore the potential for further synthesis of data mining algorithms and data visualization techniques. This incisive, groundbreaking research is sure to wield a strong influence in subsequent efforts in both academic and corporate settings. * Details advances made by leading researchers from the fields of data mining, data visualization, and statistics.* Provides a useful introduction to the science of visualization, sketches the current role for visualization in data mining, and then takes a long look into its mostly untapped potential.* Presents the findings of recent international KDD workshops as formal chapters that together comprise a complete, cohesive body of research.* Offerss compelling and practical information for professionals and researchers in database technology, data mining, knowledge discovery, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, information retrieval, high-performance computing, and data visualization.

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Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) Review

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(More customer reviews)A survey of remarkable breadth, listing hundreds of sources: 50-100 items per chapter, majority of them from conference proceedings. Evidently, this kind of volume does not let the author discuss any particular paper or topic in detail; at best, a reference is covered with a short paragraph. This does create a problem: the book's value is exhausted once the reader looks up the topic of interest, and moves on to the suggested references. "Temporal data mining" could do with a little more editing, but I am quite impressed with it the way it is, an authoritative and wide-ranging introduction to an interesting topic.

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Social Network Data Analytics Review

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(More customer reviews)This is a very interesting book for both researchers and practitioners in computer science who work in the area of data mining and want to learn the state-of-the-art in social network data analytics. The book provides good coverage of the subject area by focusing on popular research topics, such as the study of the statistical properties that are apparent in "typical" social networks, the problems of community detection and social influence analysis, the expert-location discovery problem, the privacy issues that arise in the context of social networks, as well as visualization techniques, text mining techniques and social tagging. The emerging area of integrating sensors and social networks is also examined. Each chapter of the book contains numerous bibliographic references that will guide readers who are interested in particular topics to explore these topics in more depth. Overall, I highly recommend this book!

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Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes.

Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book.

This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

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Financial Modelling in Python (The Wiley Finance Series) Review

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(More customer reviews)I am a quant, dealing with financial modeling on daily basis, and this book is the worst that I have ever used.
I had great expectations because I love python but I was so disappointed.
It is really complicated to use the CD and the explanations are so poor.
The book is mostly full with code without real explanations.
Don't buy this book and don't waste your money. It is very bad.
I dont like to write bad reviews, actually it is my first time but this book is very bad.

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"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims."–David Louton, Professor of Finance, Bryant University
This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language.
It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided.
Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point.
The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.
The book is accompanied by a CD ROM containing a code library; and a companion website www.wiley.com/go/fletcher_python which will feature code-based updates relating to Python 3.0.

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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) Review

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(More customer reviews)Stanford professor, Daphne Koller, and her co-author, Professor Nir Friedman, employed graphical models to motivate thoroughgoing explorations of representation, inference and learning in both Bayesian networks and Markov networks. They do their own bidding at the book's web page, [...], by giving readers a panoramic view of the book in an introductory chapter and a Table of Contents. On the same page, there is a link to an extensive Errata file which lists all the known errors and corrections made in subsequent printings of the book - all the corrections had been incorporated into the copy I have. The authors painstakingly provided necessary background materials from both probability theory and graph theory in the second chapter. Furthermore, in an Appendix, more tutorials are offered on information theory, algorithms and combinatorial optimization. This book is an authoritative extension of Professor Judea Pearl's seminal work on developing the Bayesian Networks framework for causal reasoning and decision making under uncertainty. Before this book was published, I sent an e-mail to Professor Koller requesting some clarification of her paper on object-oriented Bayesian networks; she was most generous in writing an elaborate reply with deliberate speed.

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A general framework for constructing and using probabilistic models ofcomplex systems that would enable a computer to use available information for makingdecisions.

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Healthcare Informatics: Improving Efficiency and Productivity Review

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(More customer reviews)With a real need for "healthcare informatics", this is an excellent book and a must read...
"As a 20+ year healthcare IT professional working specifically on electronic health record (EHR) applications as well as the ongoing analysis of this data, I have come to realize the immense problems the healthcare industry is facing today because of the ongoing proliferation of data. With the seemingly endless stream of patient, doctor, healthcare provider, insurance company, and drug manufacture data, the industry has created a real void as well as an opportunity to tap into the huge data store in an attempt to bring real positive change regarding needed efficiencies to the industry. I have found that being able to frame this real life problem has been a challenge until reading Dr. Stephen Kudyba's excellent text "Healthcare Informatics: Improving Efficiency and Productivity".
I was amazed at how quickly and precisely Dr. Kudyba's text not only framed the problem but discussed possible solutions to tackling the problem as well as provided real life examples of applications and processes in use today which are addressing the problem head-on. He does a great job of going beyond theory to actual practice by identifying informatics applications that have been incorporated by healthcare organizations. Two examples in particular from the book which resonated with me was the example of an informatics project that turned a healthcare system's paper-based resources into digital assets as well as the example of the E-commerce self-service patient check-in application which was implemented at a New Jersey hospital. Honestly its one thing to discuss problems and propose possible solutions, but Dr Kudyba's book goes beyond that by identifying "real" solutions in use today.
Lastly, I found that this text serves as a groundbreaking, foundational textbook for a potential graduate course in healthcare data informatics. We cannot ignore the fact that informatics is at the heart of correcting many of the "ills" in healthcare, and because of this, Dr Kudyba's book is a must read!


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Petri Net Theory and the Modeling of Systems Review

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(More customer reviews)Thank you for your book, James! It's invaluble source of information necessary to start. It's like "The C Programming Language" by Brian Kernigan, "The art of computer programming" by Donald Knuth. Undoubtly this book is monument in the Petri Nets area. Everyone, who wants to work in the asynchronous systems modelling field, MUST READ this book FROM COVER TO COVER.

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Modeling Online Auctions (Statistics in Practice) Review

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(More customer reviews)This book can be read as a sequel to an earlier text, Statistical Methods in e-Commerce Research (Statistics in Practice). That looked at various types of websites, like Amazon and Wikipedia, while the current offering specialises to the key case of studying auction websites. Of these, eBay dominates the discussion, simply because it is the largest such website on the Internet. In a way, the exposition is simpler than it might have been just a few years ago, when Amazon and Yahoo also ran auctions. They were keying off eBay's success and hoped to take some of that business. But their efforts came to nought, so that now if you study online auctions, it is really only eBay and a handful of much smaller entities like uBid.
The screen captures in chapter 2 of typical web pages from an auction [on eBay] shows the complex spaghetti-like source code. Note that if you do decide to screen scrape, then this is brittle, since if the website makes just minor changes in the format of their pages, extensive changes to your parsing might be necessary, to extract the same information. But as the authors make clear, screen scraping has the advantage of being free. An alternative is to use a Web Service, if that is offered by the website. Much more efficient and robust. But not all websites have this, and those that do could require payment. Plus the information offered by their Web Service might not include data that you need.
Chapter 3 tackles the problem of how to simulate data using continuous distributions, when actual auction data often looks like a mixture of continuous and spiky inputs, where the latter are bids of high frequency, that stand significantly above the rest of the bid distribution.
Chapter 4 discusses various models - exponential, log, logistic and inverse logistic, that can be used to model a given auction. But the problem is that across a set of auctions, even for instances of the same item being offered, all such models might be observed. Where a specific auction could be best fitted by a log, say, while another auction looks like a logistic. The authors suggest a "Beta()" function that has only 2 parameters. This turns out to be easy to compute, and, depending on the choices of parameter values, can replicate each of the 4 earlier models. Just as importantly, the fitting of a Beta to a given auction can be automated, which gets around an earlier problem of having to make a manual choice between one of the earlier models.
Perhaps as interestingly, the chapter goes further, into studying what the book calls the spatial similarity between auctions. The spatial refers to auctions where items differ slightly. For example, a gaming computer that has different colours, and different disk sizes and different memory sizes installed. This is a multidimensional feature space, where the features might differ continuously [like memory] or discretely [like colour]. It reflects the well known attraction of eBay where if you search for a popular item, you can find hundreds [or even thousands] that differ in parameters like these. Modelling the behaviour of bidders when confronted by a surfeit of choices would be good. The authors show a way to tackle how to define a metric in the feature space.

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Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data
Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data.
The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include:
Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site
Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks
Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies
The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data
Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems

Throughout the book, R and MATLAB software are used for illustrating the discussed techniques. In addition, a related Web site features many of the book's datasets and R and MATLAB code that allow readers to replicate the analyses and learn new methods to apply to their own research.
Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes.
Visit this book's companion website by clicking here

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