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
Table of Contents
Chapter 1 Data Mining and Analysis
Part I Data Analysis Foundations
Chapter 2 Numeric Attributes
Chapter 3 Categorical Attributes
Chapter 4 Graph Data
Chapter 5 Kernel Methods
Chapter 6 High-Dimensional Data
Chapter 7 Dimensionality Reduction
Part II Frequent Pattern Mining
Chapter 8 Itemset Mining
Chapter 9 Summarizing Itemsets
Chapter 10 Sequence Mining
Chapter 11 Graph Pattern Mining
Chapter 12 Pattern and Rule Assessment
Part III Clustering
Chapter 13 Representative-based Clustering
Chapter 14 Hierarchical Clustering
Chapter 15 Density-based Clustering
Chapter 16 Spectral and Graph Clustering
Chapter 17 Clustering Validation
Part IV Classification
Chapter 18 Probabilistic Classification
Chapter 19 Decision Tree Classifier
Chapter 20 Linear Discriminant Analysis
Chapter 21 Support Vector Machines
Chapter 22 Classification Assessment
Book Details
Title: Data Mining and Analysis: Fundamental Concepts and Algorithms
Author: Mohammed J. Zaki, Wagner Meira Jr.
Length: 550 pages
Edition: 1
Language: English
Publisher: Cambridge University Press
Publication Date: 2014-02-28
ISBN-10: 0521766338
ISBN-13: 9780521766333
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适用于初学者。第一章必读,帮你复习下所需的统计、概率以及线代知识。
PDF下载回复可见,只需1流量币。
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