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URL :  Data Mining and Analysis: Fundamental Concepts and Algorithms
Author : Mohammed J. Zaki - Wagner Meira Jr.
Detail : 2014 | PDF | 9.9 Mb | 604 pages

eBook License: Public Access from http://www.dataminingbook.info/pmwiki.php/Main/BookDownload

The main parts of the book include exploratory data analysis, frequent pattern mining, clustering, and classification. The book lays the basic 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 integrates concepts from related disciplines such as machine learning and statistics and is also ideal for a course on data analysis. Most of the prerequisite material is covered in the text, especially on linear algebra, and probability and statistics.

The book includes many examples to illustrate the main technical 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 readers use their favorite data analysis and mining software to work through our examples and to implement the algorithms we describe in text; we recommend the R software or the Python language with its NumPy package. (From Author)

eBook Contents

Data Mining and Analysis - Numeric Attributes - Categorical Attributes - Graph Data - Kernel Methods - High-dimensional Data - Dimensionality Reduction - Itemset Mining - Summarizing Itemsets - Sequence Mining - Graph Pattern Mining - Pattern and Rule Assessment - Representative-based Clustering - Hierarchical Clustering - Density-based Clustering - Spectral and Graph Clustering - Clustering Validation - Probabilistic Classification - Decision Tree Classifier - Linear Discriminant Analysis - Support Vector Machines - Classification Assessment
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