Data Mining and Machine Learning: Fundamental Concepts and Algorithms

✍ Scribed by Mohammed J. Zaki, Wagner Meira Jr

• Publisher: Cambridge University Press
• Year: 2020
• Tongue: English
• Leaves: 777
• Edition: 2
• Category: Library

✦ Synopsis

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

Cover
Frontmatter
Contents
Preface
PART ONE - DATA ANALYSIS FOUNDATIONS
CHAPTER 1 - Data Matrix
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 TWO - 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 THREE - 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 FOUR - CLASSIFICATION
CHAPTER 18 - Probabilistic Classification
CHAPTER 19 - Decision Tree Classifier
CHAPTER 20 - Linear Discriminant Analysis
CHAPTER 21 - Support Vector Machines
CHAPTER 22 - Classification Assessment
PART FIVE - REGRESSION
CHAPTER 23 - Linear Regression
CHAPTER 24 - Logistic Regression
CHAPTER 25 - Neural Networks
CHAPTER 26 - Deep Learning
CHAPTER 27 - Regression Evaluation
Index