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Data Mining: Concepts, Models, Methods, and Algorithms, by Mehmed Kantardzic
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This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The�goal of this book is to�provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
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If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com
If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: pressbooks@ieee.org
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- Sales Rank: #1479895 in Books
- Published on: 2011-08-16
- Original language: English
- Number of items: 1
- Dimensions: 9.23" h x 1.34" w x 6.44" l, 2.00 pounds
- Binding: Hardcover
- 552 pages
Review
“I therefore gladly salute the second editing of this lovely and valuable book. Researchers, students as well as industry professionals can find the reasons, means and practice to make use of essential data mining methodologies to help their interests.”� (Zentralblatt MATH, 2012)
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From the Back Cover
Now updated—the systematic introductory guide to modern analysis of large data sets
As data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information for decision-making.
This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies:
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Support Vector Machines (SVM)—developed based on statistical learning theory, they have a large potential for applications in predictive data mining
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Kohonen Maps (Self-Organizing Maps - SOM)—one of very applicative neural-networks-based methodologies for descriptive data mining and multi-dimensional data visualizations
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DBSCAN, BIRCH, and distributed DBSCAN clustering algorithms—representatives of an important class of density-based clustering methodologies
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Bayesian Networks (BN) methodology often used for causality modeling
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Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks
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CART algorithm and Gini index in building decision trees
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Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm
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Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning
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PageRank algorithm for mining and authority ranking of web pages
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Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents
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New sections on temporal, spatial, web, text, parallel, and distributed data mining
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More emphasis on business, privacy, security, and legal aspects of data mining technology
This text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here.
This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here.
About the Author
MEHMED KANTARDZIC, PhD, is a professor in the Department of Computer Engineering and Computer Science (CECS) in the Speed School of Engineering at the University of Louisville, Director of CECS Graduate Studies, as well as Director of the Data Mining Lab. A member of IEEE, ISCA, and SPIE, Dr. Kantardzic has won awards for several of his papers, has been published in numerous referred journals, and has been an invited presenter at various conferences. He has also been a contributor to numerous books.
Most helpful customer reviews
18 of 18 people found the following review helpful.
Survey, not how-to
By wiredweird
The subtitle advertises "concepts, models, methods, and algorithms". Concepts and models, yes; methods, a few; algorithms, nearly none that you could actually code.
This book's strength is its breadth. It offers brief tastes of many topics. It discusses early data preparation, including reduction of dimension and handling of outliers and missing values. It emphasizes that different kinds of questions must be addressed in different ways. The rest of the book then covers decision rules of different sorts, clustering, neural networks, genetic algorithms, fuzzy logic, and data visualization. Each chapter includes references and comments on what to expect from each reference - a nice touch. The end of the book names a wide variety of web sites, products, and companies dedicated to data mining.
The big problem, however, is that the book is shallow. With a few exceptions, it just names techniques instead of giving descriptions that a programmer can use. For example, the discussion of missing data barely mentions the idea that imputed (made-up) values must be tailored to the specific analysis technique, so as to minimize their effect on results. There are exceptions, of course. Neural nets get a relatively detailed treatment. The author gives illustrative examples of genetic algorithms, but those were thin and tangential to data mining. The section on data visualization could have been much more lively. There is a huge body of visual technique, some bordering on artistry, that can present high-dimensional data to the human pattern-detection faculty, and samples are readily available. This book's examples were small and drab, though. Also, it completely ignored the research in auditory and tactile data representation, and omitted discussion of graphic design principles required for effective presentation.
What really bothered me were examples of sheer carelessness. A number of figures, including 4.8 and 9.9, contain errors severe enough to interfere with the point being made. Important relationships are simply illegible. Books like this aren't cheap - I would have hoped that the author would show a little more respect for the people paying the money.
This book may have value as a survey resource, but isn't for the reader who wants to implement the algorithms. Its bibliography is informative, but not a major asset. Indices of current products and web sites nearly guarantee early obsolescence. Look this over thoroughly before you commit your time and money to it.
4 of 4 people found the following review helpful.
Pattern recognition or machine learning, not data mining
By Y. Keselman
This book can be used as an introduction to pattern recognition or machine learning rather than into data mining. Data mining does appear here and there, but mostly it is the classical pattern recognition and machine learning material (data reduction, clustering, neural networks) with very few illustrations from data mining. An introduction into genetic algorithms and fuzzy sets is also in the book, just in case, I suppose. If you'd like more specific data mining knowledge, look elsewhere.
2 of 2 people found the following review helpful.
Used this book for the Data mining class that is ...
By Christopher Del Fattore
Used this book for the Data mining class that is taught by the author at university of Louisville. I found it hard to learn for the book. There is very little examples for the formulas that are presented in each of the chapters.
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