# First Edition

**Data Mining and Analysis: Fundamental Concepts and Algorithms**

*Mohammed J. Zaki and Wagner Meira, Jr*

## 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.

### Key features

Covers both core methods and cutting-edge research

Algorithmic approach with open-source implementations

Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas

Short, self-contained chapters with class-tested examples and exercises allow for flexibility in designing a course and for easy reference

Supplementary website with lecture slides, videos, project ideas, and more

You can find here the table of contents and errata for the first edition.

If you find the book useful please consider submitting a review on Amazon, and cite us as follows:

Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333.

## Reviews & Endorsements

*This book by Mohammed Zaki and Wagner Meira, Jr. is a great option for
teaching a course in data mining or data science. It covers both
fundamental and advanced data mining topics, explains the mathematical
foundations and the algorithms of data science, includes exercises for
each chapter, and provides data, slides and other supplementary material
on the companion website.*

**Gregory Piatetsky-Shapiro**

*World-class experts, providing an encyclopedic coverage of all data
mining topics, from basic statistics to fundamental methods (clustering,
classification, frequent itemsets), to advanced methods (SVD, SVM,
kernels, spectral graph theory). For each concept, the book thoughtfully
balances the intuition, the arithmetic examples, as well the rigorous
math details. It can serve both as a textbook, as well as a reference
book.*

**Professor Christos Faloutsos**