Ebook Free Introduction to Data Mining
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Introduction to Data Mining
Ebook Free Introduction to Data Mining
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Product details
Hardcover: 792 pages
Publisher: Pearson; 1 edition (May 12, 2005)
Language: English
ISBN-10: 0321321367
ISBN-13: 978-0321321367
Product Dimensions:
7.7 x 1.6 x 9.4 inches
Shipping Weight: 3.1 pounds (View shipping rates and policies)
Average Customer Review:
3.6 out of 5 stars
61 customer reviews
Amazon Best Sellers Rank:
#109,200 in Books (See Top 100 in Books)
I bought the hardcover version of this book. and it cost 162 dollars.this book use the CHEAPTEST MATERIAL in all my text books (and i have multiple Masters degrees so i have a lot of books). I simply cannot accept book cost over 162 dollar use the same type of paper you find in a 2 dollar book. the paper are thin and yellowish, reminds me the book i use to use back in 80th.i also bought another book "data mining concept and techniques", cost 50 dollar and actually feel like a premium book.we all know books are extremely over priced by like a million percent (thus the "india edition" text books only cost like a quarter of the price). i just hope the publisher spend a few dollars on improving the paper and print quality of the book. do they really need to save 5 dollar on a 160 dollar of pure profit?
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.
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.
I really appreciated the annotated bibliography at the end of each chapter. Potential readers should be aware that this is a survey text, but if you are looking for introductory material on data mining, with lots of great references on where to go for deeper reading in selected topics, this is a good place to start.
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!
This textbook is extremely dry and focuses on many aspects of data mining that are not applicable to practical applications. Would instead recommend a better statistics book coupled with a machine learning text.
This book was recommended by my Machine Learning professor. and has a wealth of information on clustering, belief networks, decision trees and clustering.
Tan, Steinbach and Kumar did a good job. It is definitely a good read on Data Mining (DM). Pretty much all the fundamental DM algorithms are covered and explained in simplistic fashion. Most of the high level DM algorithms are basically off shoots of one or more of these fundamental DM algorithms, so it is imperative to have a clear understanding of them. This book is very helpful in that regard. It would have been nice if the book covered Support Vector Machine in more detail.Undergraduate level Statistics and Linear Algebra knowledge is needed to understand some concepts covered in the book.Good luck future miners.
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