Tan, Pang-Ning
Introduction to Data Mining / Pang-Ning Tan, Michael Steinbach and Vipin Kumar - New Delhi Pearson 2018 - xix, 760p. : ill. ; 24cm
It includes index.
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples.
Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
Table of Content
Chapter 1 Introduction
Chapter 2 Data
Chapter 3 Exploring Data
Chapter 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation
Chapter 5 Classification: Alternative Techniques
Chapter 6 Association Analysis: Basic Concepts and Algorithms
Chapter 7 Association Analysis: Advanced Concepts
Chapter 8 Cluster Analysis: Basic Concepts and Algorithms
Chapter 9 Cluster Analysis: Additional Issues and Algorithms
Chapter 10 Anomaly Detection
Salient Features
1. Provides both theoretical and practical coverage of all data mining topics
2. Includes extensive number of integrated examples and figures
3. Offers instructor resources including solutions for exercises and complete set of lecture slides
4. Assumes only a modest statistics or mathematics background without any requirement of database knowledge
5. Important topics such as predictive modeling, association analysis, clustering, anomaly detection, visualization covered"
9789332571402
Data Mining; Database Management; Knowledge Acquisition (Expert Systems)
006.312 TAN
Introduction to Data Mining / Pang-Ning Tan, Michael Steinbach and Vipin Kumar - New Delhi Pearson 2018 - xix, 760p. : ill. ; 24cm
It includes index.
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples.
Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
Table of Content
Chapter 1 Introduction
Chapter 2 Data
Chapter 3 Exploring Data
Chapter 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation
Chapter 5 Classification: Alternative Techniques
Chapter 6 Association Analysis: Basic Concepts and Algorithms
Chapter 7 Association Analysis: Advanced Concepts
Chapter 8 Cluster Analysis: Basic Concepts and Algorithms
Chapter 9 Cluster Analysis: Additional Issues and Algorithms
Chapter 10 Anomaly Detection
Salient Features
1. Provides both theoretical and practical coverage of all data mining topics
2. Includes extensive number of integrated examples and figures
3. Offers instructor resources including solutions for exercises and complete set of lecture slides
4. Assumes only a modest statistics or mathematics background without any requirement of database knowledge
5. Important topics such as predictive modeling, association analysis, clustering, anomaly detection, visualization covered"
9789332571402
Data Mining; Database Management; Knowledge Acquisition (Expert Systems)
006.312 TAN