Lecture notes in data mining

The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This...

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Bibliographic Details
Main Authors: Berry, Michael W, Browne, Murray
Format: eBook Book
Language:English
Published: Hackensack, N.J World Scientific Publishing Co. Pte. Ltd 2006
World Scientific
World Scientific Publishing Company
WORLD SCIENTIFIC
World Scientific Publishing
Edition:1
Subjects:
Artificial Intelligence (Machine Learning, Neural Networks, Fuzzy Logic)
Computer Systems (Database Systems, Operating Systems)
Data mining
Numerical & Computational Mathematics
Probability & Statistics
SCIENCE
Association Rules
Data Mining
Algorithms
Clustering
Lecture Notes
Classification
ISBN:9812568026, 9789812568021, 9789812773630, 9812773630
Online Access:Get full text
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Table of Contents:
  • Lecture notes in data mining -- Preface -- Contents -- Chapter 1: Point estimation algorithms -- Chapter 2: Applications of Bayes Theorem -- Chapter 3: Similarity measures -- Chapter 4: Decision trees -- Chapter 5: Genetic algorithms -- Chapter 6: Classification: distance-based algorithms -- Chapter 7: Decision tree-based algorithms -- Chapter 8: Covering (rule-based) algorithms -- Chapter 9: Clustering: an overview -- Chapter 10: Clustering: hierarchical algorithms -- Chapter 11: Clustering: partitional algorithms -- Chapter 12: Clustering: large databases -- Chapter 13: Clustering: categorical attributes -- Chapter 14: Association rules: an overview -- Chapter 15: Association rules: parallel and distributed algorithms -- Chapter 16: Association rules: advanced techniques and measures -- Chapter 17: Spatial mining: techniques and algorithms -- References -- Index
  • 2. Motivation -- 3. Requirements for Scalable Clustering -- 4. Major Approaches to Scalable Clustering -- 5. BIRCH -- 6. DBSCAN -- 7. CURE -- 8. Summary -- 13 Clustering: Categorical Attributes -- 1. Introduction -- 2. Motivation -- 3. ROCK Clustering Algorithm -- 4. COOLCAT Clustering Algorithm -- 5. CACTUS Clustering Algorithm -- 6. Summary -- 14 Association Rules: An Overview -- 1. Introduction -- 2. Motivation -- 3. Association Rule Process -- 4. Large Itemset Discovery Algorithms -- 5. Summary -- 15 Association Rules: Parallel and Distributed Algorithms -- 1. Introduction -- 2. Motivation -- 3. Parallel and Distributed Algorithms -- 4. Discussion of Parallel Algorithms -- 5. Summary -- 16 Association Rules: Advanced Techniques and Measures -- 1. Introduction -- 2. Motivation -- 3. Incremental Rules -- 4. Generalized Association Rules -- 5. Quantitative Association Rules -- 6. Correlation Rules -- 7. Measuring the Quality of Association Rules -- 8. Summary -- 17 Spatial Mining: Techniques and Algorithms -- 1. Introduction and Motivation -- 2. Concept Hierarchies and Generalization -- 3. Spatial Rules -- 4. STING -- 5. Spatial Classification -- 6. Spatial Clustering -- 7. Summary -- References -- Index
  • Intro -- CONTENTS -- Preface -- 1 Point Estimation Algorithms -- 1. Introduction -- 2. Motivation -- 3. Methods of Point Estimation -- 4. Measures of Performance -- 5. Summary -- 2 Applications of Bayes Theorem -- 1. Introduction -- 2. Motivation -- 3. The Bayes Approach for Classification -- 4. Examples -- 5. Summary -- 3 Similarity Measures -- 1. Introduction -- 2. Motivation -- 3. Classic Similarity Measures -- 4. Example -- 5. Current Applications -- 6. Summary -- 4 Decision Trees -- 1. Introduction -- 2. Motivation -- 3. Decision Tree Algorithms -- 4. Example: Classification of University Students -- 5. Applications of Decision Tree Algorithms -- 6. Summary -- 5 Genetic Algorithms -- 1. Introduction -- 2. Motivation -- 3. Fundamentals -- 4. Example: The Traveling-Salesman -- 5. Current and Future Applications -- 6. Summary -- 6 Classification: Distance-based Algorithms -- 1. Introduction -- 2. Motivation -- 3. Distance Functions -- 4. Classification Algorithms -- 5. Current Applications -- 6. Summary -- 7 Decision Tree-based Algorithms -- 1. Introduction -- 2. Motivation -- 3. ID3 -- 4. C4.5 -- 5. C5.0 -- 6. CART -- 7. Summary -- 8 Covering (Rule-based) Algorithms -- 1. Introduction -- 2. Motivation -- 3. Classification Rules -- 4. Covering (Rule-based) Algorithms -- 5. Applications of Covering Algorithms -- 6. Summary -- 9 Clustering: An Overview -- 1. Introduction -- 2. Motivation -- 3. The Clustering Process -- 4. Current Applications -- 5. Summary -- 10 Clustering: Hierarchical Algorithms -- 1. Introduction -- 2. Motivation -- 3. Agglomerative Hierarchical Algorithms -- 4. Divisive Hierarchical Algorithms -- 5. Summary -- 11 Clustering: Partitional Algorithms -- 1. Introduction -- 2. Motivation -- 3. Partitional Clustering Algorithms -- 4. Current Applications -- 5. Summary -- 12 Clustering: Large Databases -- 1. Introduction