Introduction to Data Mining: Pearson New International Edition PDF eBook by Pang-Ning Tan (PDF)

Description

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐A good general summary of various areas of data mining, with a few sections that give the opportunity to go more in depth. It gives more basics than specifics, so while it may be something a person would keep around to reference, there may be more comprehensive guides people would go with.It was an enjoyable read; neither overly dense or too simplified. And it comes in paperback so weaklings like me don’t strain to lift it

⭐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.

⭐Good reference on DM. Does not focus on any specific programming language.

⭐Book is in English apart from cover. As far as I can tell same contents as US copy, just smaller size. Paper is practically see thru, print is quite small, but price was half $$. Several Chapters of the book can be read online at publishers site.

⭐The book quality is good . But this is not the us edition , everything is same but some exercises are different . Otherwise , easy to read , well explained

⭐ordered for a class, very helpful during the class

⭐This is a strong overview of machine learning techniques for the mathematically inclined.

⭐This book was recommended by my Machine Learning professor. and has a wealth of information on clustering, belief networks, decision trees and clustering.

⭐It’s a great book for data mining – lots of pretty examples covering clustering, classification, and some other concepts too. There are some exercises after each chapter, but the answers are not included within this book. My course didn’t go into too much detail about the topics covered in this book, but it was still a nice alternative to reading my university’s lecture slides (which tend to lack some information). You will need to find a free PDF document online somewhere with the answers in it (not hard, just google it).

⭐Great content, really! But the organization of information is the worst I’ve ever seen in a book. Data mining is a lot about structuring data before you process it. The authors miss this point in writing a book: There is only one page table of contents for ~713 pages of complex knowledge. There are no pages given when referring to other sections of the book. The funniest part is the index: It is made automatically by some stupid algorithm and the reader has to bet which page of the often ~30 given different sections per keyword given is the right one 😀

⭐Tan, Steinbach and Kumar have authored a very good book on the elements of data mining (data science). If you have a degree in mathematics and comfortable with computational aspects with a curious mind for data mining, then this book is for you! The authors take a deep dive and seamlessly merge the concepts from linear algebra, calculus and matrix operations with computational aspects such as databases, data handling, and logical processing to present a very comprehensive foundation of data science methods. You’ll be particularly impressed with the depth of coverage with respect to SVMs, non-classical methods such as ensemble techniques and addressing the class imbalance problems. The authors go into details on (seldom used) sequence analysis, infrequent pattern analysis, BIRCH and OPOSSUM variations in clustering and subgraph analysis methods. Of particular interest is the chapter on outlier analysis (anomaly detection) which is handled very well. Some readers will miss the treatment of genetic algorithms which is kind of mentioned in passing.However, a glaring omission is the treatment of regression concepts; perhaps because the authors feel that regression concepts have been (and are) widely covered in other books that they’ve focused on the more esoteric aspects. But to truly appreciate this book one is expected to have a strong foundation, and a high level of comfort, in advanced mathematics. Overall it’s a very good book.

⭐Mostly focuses on unsupervised learning methods , suitable for a general approach towards machine learning. For a deeper understanding of supervised models , different approach is required. Good concepts related to basics of ML like linear algebra and dimension reduction

⭐Most colleges are preferring this book as a textbook for Datamining.

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Introduction to Data Mining: Pearson New International Edition PDF eBook 2013 PDF Free Download
Download Introduction to Data Mining: Pearson New International Edition PDF eBook PDF
Free Download Ebook Introduction to Data Mining: Pearson New International Edition PDF eBook