Data Mining: Concepts and Techniques

Rating:
Summary: Further editions are warranted.
Review: In a nutshell, this book is an ambitious attempt to introduce the reader to the key "concepts and techniques" of Data Mining from the viewpoint of a computer science professional. While perhaps not quite yet the "master reference that practitioners and researchers have long been seeking", this book clearly has the potential to become the "obvious choice for academic and professional classrooms". From a data mining practitioner's point of view, the greatest strength of this book may be the coverage of data management topics such as OLTP/OLAP, data warehousing, data preprocessing, and data conceptualization. That said, this book also includes some room for improvement. Specifically, the inconsistent level of clarity and sometimes not-so-logical structure of the book can make it a challenging read, even for seasoned data mining practitioners. Nevertheless, with some careful editing with respect to accuracy and clarity, further editions of this book would likely be appreciated by those who are interested in the fascinating world of data mining.

Rating:
Summary: Further editions are warranted.
Review: In a nutshell, this book is an ambitious attempt to introduce the reader to the key "concepts and techniques" of Data Mining from the viewpoint of a computer science professional. While perhaps not quite the "master reference that practitioners and researchers have long been seeking", this book clearly has the potential to become the "obvious choice for academic and professional classrooms".
In my opinion, the greatest strength of this book is the coverage of data management topics including OLAP/OLTP, data warehousing, data preprocessing, and data conceptualization.
That said, this book also includes some room for improvement. Specifically, the inconsistent level of clarity and less than perfect structure of the book can make it a somewhat challenging read, even for seasoned "practitioners and researchers" alike. Nevertheless, with some careful editing with respect to accuracy and clarity, further editions of this book would likely be appreciated by those who are interested in the fascinating world of data mining.

Rating:
Summary: Best introduction I know
Review: It is very easy to collect huge volumes of data - social statistics, bank records, biological data, and more - but very hard to pull useful facts out of the heap. This book is about processing large volumes of data in ways that let simple descriptions emerge.

This is an introductory level book, aimed at someone with reasonably good programming skills. A little facility with statistics might help, but certainly isn't necessary. The book starts gently, with some very basic questions: what is data mining exactly, when there seem to be so many definitions for the term? What is a data warehouse, and how does it differ from a database? Next, the authors address the data itself in terms of quality, usability, and organization for efficient access. The central chapters, 4 thhrough 8, address various kinds of query specification, kinds of relationships to extract, correlations, clustering, and classification. None of the discussions is especially deep. All, however, are presented in pseudocode or simple math that can easily be translated into working code. The careful reader learns a few basic principles that work well in many contexts: entropy maximization, Bayesian analysis, and simple stats. It may be surprising to see how little of normal statistical analysis is used. I suspect the authors assume that stats-savvy readers will already know how to apply significance testing, and that stats-naive readers don't need the distraction. The last chapters discuss complex data, where the best structure for the data and the questions to be asked of it are not at all obvious, and tools and applications used in data mining.

The book is nicely laid out as a textbook, with an orderly summary, problem set, and bibliography at the end of each chapter. The bibliography is more than just a list of names and authors - it actually helps the reader decide which references will give the best description of each of the chapter's topics.

This is a clear, usable introduction to data mining: the data it uses, the questions it answers, and the techniques for connecting them. It gives codable detail for lots of techniques, and prepares the reader for more advanced discussions. I recommend it very highly.

//wiredweird

Rating:
Summary: A Great Book for Data Mining Researchers!
Review: Jiawei Han is an influential and prominent figure in the data mining scene and this book tells you why this is so. Everything you need to know about data mining as a researcher can be found in this comprehensive book! There are good examples and abundant references as well as discussions on important algorithms found in various data mining techniques such as clustering, classification and association rule mining. Even recent extensions of the mining methodologies to the spatial, temporal and web domains are discussed as well. All in all, it is an ideal book for a data mining researcher. I refer to it all the time as I write papers and reports!

Rating:
Summary: The *real* textbook on the technical aspects of data mining
Review: There are a number of books on data mining. The vast majority of them are non-technical in the sense that they talk a great deal about how data mining is a glorious area, without ever getting into the nitty gritty of how data mining algorithms actually work. There are also a couple of technical textbooks on data mining that are nothing more than mistitled books on machine learning (yes, I know, the ML arena does contribute a lot towards data mining). This is the first true textbook on data mining algorithms and techniques. It covers a vast array of topics and does ample justice to the vast majority of them. In fact, it even covers semi-automated (OLAP) technologies for data mining. The book consistently uses data from a single (fictitious) organization to illustrate most concepts. This gives a strong sense of cohesion to can actually be very different techniques. One key aspect of the book is its question-and-answer format. The main arguments in favor of such a format are (1) it is a clean way introduce a new topic or concept (2) students love it when things are laid out for them. On the other hand, such an approach seems inappropriate for a graduate level text. This book is certain to become "the standard" data mining textbook.

Rating:
Summary: Very little content
Review: This book uses all the buzz words, but has little content. It discuss too many topics and does not go into detail. The writing is very difficult to understand. It is very disappointing especially since the author is well known in the field.

Rating:
Summary: A principal book for subject field overview
Review: This book was essencial for me to dig deeper into data mining techniqes and methonds. It is a good guidance book for beginners and also for advanced practictioners and researchers. It covers systematically all major themes on data mining and provides additional references for briefly covered topics. My subject area is web mining. I found this book as an overview book. I could get a wide view of the field. I got good hints for my specific field. It is very strictly written book not preferring this or other products as several comercial books of today do. The book is very up to date. I would say it is a current bible of data mining science, though there could be some similar or even better books on the subject in last month but maybe I'm not up to date.

Rating:
Summary: A book that you can learn nothing.
Review: This is a book that surveys the all the commom ideas in data mining without teaching any of them. I only recommend this book for those who just want to see what's going on in data mining but do not need to learn anything.

Rating:
Summary: Like to read it too much
Review: This textbook explains about concepts of Data Warehousing , OLAP, and Data Mining as well. The key algorithms and theory is described such Decision Tree Learning, Neural Networks and Sequences Pattern Mining. The example is very easy to understand. Also several approaches of Text Mining, Bio Mining and Spatial Mining is introducted. So the book's content is very well

Rating:
Summary: Very complicated reading
Review: Very bad English and extremely difficult to read. I will definitely not recommend this book to anyone unless he is an expert in data mining.