Author(s) Name:  Mohammed J. Zaki, Wagner Meira Jr

About the Book:

   The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners.
   The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

Table of Contents

Data mining and analysis

Data Analysis Foundations

  1. Numeric attributes
  2. Categorical attributes
  3. Graph data
  4. Kernel methods
  5. High-dimensional data

Frequent Pattern Mining

  1. Itemset mining
  2. Summarizing itemsets
  3. Sequence mining
  4. Graph pattern mining

Clustering

  1. Representative-based clustering
  2. Hierarchical clustering
  3. Density-based clustering
  4. Spectral and graph clustering

Classification

  1. Probabilistic classification
  2. Decision tree classifier
  3. Linear discriminant analysis
  4. Support vector machines

Regression

  1. Linear regression
  2. Logistic regression
  3. Neural networks
  4. Deep learning
  5. Regression evaluation

ISBN:  9780521766333

Publisher:  Cambridge University Press New York

Year of Publication:  2014

Book Link:  Home Page Url