DATA MINING AND ANALYSIS: Fundamental Concepts and Algorithms
Edited by : MOHAMMED J. ZAKI and WAGNER MEIRA JR
This book is an outgrowth of data mining courses at Rensselae r Polytechnic Institute (RPI) and Universidade Federal de Minas Gerais (UFMG); the R PI course has been offered every Fall since 1998, whereas the UFMG course has be en offered since 2002. Although there are several good books on data mining an d related topics, we felt that many of them are either too high-level or too advanc ed. Our goal was to write an introductory text that focuses on the fundamental a lgorithms in data mining and analysis. It lays the mathematical foundations for the c ore data mining methods, with key concepts explained when first encountered; the boo k also tries to build the intuition behind the formulas to aid understanding. The main parts of the book include exploratory data analysis , frequent pattern mining, clustering, and classification. The book lays the ba sic foundations of these tasks, and it also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. It integrat es concepts from related disciplines such as machine learning and statistics and is a lso ideal for a course on data analysis. Most of the prerequisite material is covered in th e text, especially on linear algebra, and probability and statistics. The book includes many examples to illustrate the main techn ical concepts. It also has end-of-chapter exercises, which have been used in class . All of the algorithms in the book have been implemented by the authors. We suggest that re aders use their favorite data analysis and mining software to work through our exampl es and to implement the algorithms we describe in text; we recommend the R software o r the Python language with its NumPy package.