Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki (PDF)

Description

The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.

User’s Reviews

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

⭐I can’t believe previous readers rate this book so high. This is one of the WORST Data Mining Book I’ve ever read.The book is poorly written in both logic and language. One critical function of a book, especially a text book, is to help readers to learn the knowledge. But this book only inhibits that work because one might use more than 3 times of time to learn the same knowledge from this book compared with other great data mining books. The writing skills of the author is so limited that most concept and algorithm is instructed very unsatisfactorily which leads the reader to feel confused about most things. Thus they can not learn data mining and analysis technique efficiently and clearly. So if you are interested in Data Mining but do not have a strong knowledge, stay away from this book! It will drain your interest because you’re gonna feel learning through this book is soooooo exhausting.The authors tried to make the book more “mathematical” so they added a bunch of unnecessary mathematical symbol into unnecessary equations without sufficient explanation and illustration. Further more, there is a lot of key points of data mining method missed. For example, this book does not fully explained the pros and cons of cluster methods so the reader will never know which method to use in a certain situation. As for classification part, ensemble method is omitted which is popularly used currently. There is even errors in pseudo-code. The content schedule seems draw much attention on kernel method, but I don’t think anyone may be benefit from it. Chapter 5(Kernel Method) itself is just a jungle. Anyway, it listed so much disjoint concept by perplexing equation, symbol and statement, but few of them hit the point.The slogan of this book is it covers some cutting-edge area but still good for a starter. But I can not see any cutting edge knowledge in this book. So if you are familiar with data mining and analysis, you will never need this book. And, if you are a starter, you will never get your first step by reading this book.

⭐This was a text book for a class. It covers many major topics and has practical examples that fit with open-source web sets with real data sets that you can practice on. It covers many of the major algorithms with good pseudocode that can be implemented. One thing to note is that it is very math intensive with a lot of applied probability and linear algebra.

⭐I have only read a few chapters but I have already found this to be a quite good reference. The writing style is clear and the material seems to be well organized. However, what ever you do, do not buy the Kindle version. There are countless equations which are printed so small that even on the highest text size setting you will need a magnifying glass to read them. It is a pity that such an otherwise worthwhile reference has this problem.

⭐This is absolutly the best introduction book to data mining. This is not an advanced one, as it says it is the fundamentals. It is very well written, avoiding the assumption that the reader should guess it himself. I had 3 other well known references in data mining, but I couldn’t finish reading them. My packground is in Computer Engineering. I wish I had this book before I suffered with the other. Now I may try aproaching Hastie’s book again:)

⭐Excellent book to start with the concepts of data mining. Less focus on tools and more on concepts. After covering most of the book, I’ve gotten more comfortable with the principles of ML. Good books to complement this are An Intro To Stat Learning (Hastie et al.) and R in Action.

⭐The best thing about the book is detailed examples with simple numbers than even more complicated equations. For instance, I was able to understand Kernels by reading this book better than by any book out there, because it gives 10-12 examples of various types of Kernel functions.

⭐I have a multitude of books on data mining and this is by far one of the must easy to follow books that I have seen. While the authors sacrifice scope, it more than makes up for it in clearness of exposition. I highly encourage anyone interested in data mining to add this one to their collection

⭐Fantastic book, well written

⭐Hands down the best introductory data mining book. Topics include graph mining/clustering, kernel methods, clustering, classification, and association rule mining. Sufficient Maths background and other required concepts to grasp the main topics are provided in separate chapters. Only weak point is there are not enough examples, and sometimes the methods/theorems/derivations are presented one after the other without mentioning what the applications are. Overall an excellent starting point for those looking to enter machine learning or data mining fields.

⭐Parece uma armadilha para pegar otários. O livro é bom e muito útil na área de data mining e data science. Aí o incauto vê o preço bom da edição para Kindle e compra. A decepção é imediata: completamente inútil. A diagramação, as fórmulas, tudo errado e cheio de defeitos. Não caia nessa.The book provides a very good primer on the mathematical background for data mining and foundational statistical machine learning. I am enjoying the read!

⭐math symbols not rendered in a readable way in the e-book. If there were a pdf version, that would probably have worked.

⭐The book is a great book. It explained the math behind the analysis and ming processes well for a physics major who’s now working towards data science.

Keywords

Free Download Data Mining and Analysis: Fundamental Concepts and Algorithms in PDF format
Data Mining and Analysis: Fundamental Concepts and Algorithms PDF Free Download
Download Data Mining and Analysis: Fundamental Concepts and Algorithms PDF Free
Data Mining and Analysis: Fundamental Concepts and Algorithms PDF Free Download
Download Data Mining and Analysis: Fundamental Concepts and Algorithms PDF
Free Download Ebook Data Mining and Analysis: Fundamental Concepts and Algorithms