Introduction to Data Mining

I really like this book due to how easy of a read it is. This book is meant for people from many different backgrounds (not just computer science) to learn about data mining. If you are looking for deep in depth details then you are most likely going to be a little disappointed. The examples are well thought out and the figures are all really informative. If your looking to learn about data mining then I would recommend this book to you.

We used this book in a class which was my first academic introduction to data mining.The book's strengths are that it does a good job covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection. Additional bonus appendices cover some elements of linear algebra, dimensionality reduction, probability and statistics, regression analysis, and optimization, in case those concepts are fuzzy for the student. They're by no means thorough enough to learn the topic, merely to remind the reader of salient points they should remember.I liked the structure of the book, with each analysis topic being divided into a basic concepts and algorithms chapter, followed by an additional issues and algorithms chapter.I liked that when algorithms were presented, they were presented as pseudocode rather than in any particular language.What I did not like is that separating the concepts from their applications created a bit too much distance for those wanting to apply these concepts. In our class, we were using a tool called Weka, which provides reference implementations of various data mining algorithms in Java, and sometimes it was difficult to tell what we should learn from the results of our experiments. The book did not discuss this very deeply, and certainly not against the types of results that we were getting from our application.During the course, because I knew we would be relying on Weka, I purchased a copy of ISBN-10: 0123748569 http://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569/ref=pd_bxgy_b_text_b, which was written by the group that maintains Weka. I found their book to be helpful while I ran the Weka tool, and I was able to use it to develop command line use of the tool and solve some memory management problems. This book also covers much the same ground, although from a bit more practical perspective.Later, because I'm interested in data mining in a large database environment, I purchased ISBN-10: 0123814790 http://www.amazon.com/Data-Mining-Concepts-Techniques-Management/dp/0123814790/ref=pd_bxgy_b_text_c, which is much more focused on the "how" of data mining, to include describing the use of data cubes and the necessities of processing it using data mining algorithms.I cannot complain about Tan's book, just that I wished it had slightly more thorough explanations of what one should learn as data mining is certainly an iterative process. If you're interested in Weka, I recommend the Witten book, and if you're new to data modeling as well, I recommend the Han book.

This book is amazingly well written. Everything is explained in a very clear and to-the-point style. The book can be read from front to back or used as a reference book. It contains countless diagrams and the structure of the content is immediately apparent.The book covers a lot of the important aspects of data mining. It provides algorithms and techniques for classification, clustering, association analysis, and anomaly detection. Every algorithm is not only formally stated, but also explained in a way that conveys intuition.I only wish other authors also wrote books this way.

Good

Gives an excellent "under the hood" look at how key data algorithms work. Many books on data science don't give such a thorough but simplified explanation at what's going with data science algorithms... they just tell you how to code them.Understanding how to do the algorithms BY HAND gives a much deeper and thorough understanding.

So I've only read the first chapter, and I have to say, so far, I am not impressed. As others have said, the quality of the book itself is cheap; extremely thin pages, poor printing, and no color. The color is especially disappointing as there are many graphics that would have benefitted from color. I purchased a used, hard cover copy. I did receive it in time, and it is in near new condition...well, as new as it can be for such an old book. Ultimately though, what's so unbelievable to me, is the fact that this book is15 years old! Surely data mining has evolved since the writing of this book. Unfortunately, it's a textbook for a course I'm enrolled in. Bad on my professor for selecting this, but that's on her and/or possibly the school too. This is a graduate level course and I'm having difficulties understanding why this book. I definitely plan on bringing this up to my professor. If I were buying a book to explore this subject, I would not be buying this one. It's simply too old. Technology just changes too fast, and for a 15 year old book, I can ONLY see how it can cover nothing but a rudimentary introduction of the subject. This does not require a whole book to do so, I'm sure the majority of it's contents is so dated, that it's no longer applicable. Find something else.

Although this book is considered as a standard introductory textbook for data mining classes, in my view it has limited scope. Key issues such as the logic (and perhaps some theory) behind classification and clustering techniques is not presented thoroughly, while there is an extended presentation of association analysis. This is in line with the research interests of the authors of course ( at least that is what I concluded by viewing the reference lists at the end of the chapters - the authors have published extensively in this field). The problem is that association rules are reported by other sources to be less useful than newest algorithms such as collaborative filtering. No coverage on regression exists in the book as well. So in overall I believe there are more useful books to introduce someone on this very interesting and fun field!