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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 book is a series of seventeen edited “student-authored lectures” which explore in depth the core of data mining classification, clustering and association rules) by offering overviews that include both analysis and insight.The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms.The books discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining.Sample Chapters)Chapter 1: Point Estimation Algorithms 397 KB)Contents:Point Estimation AlgorithmsApplications of Bayes TheoremSimilarity MeasuresDecision TreesGenetic AlgorithmsClassification: Distance Based AlgorithmsDecision Tree-Based AlgorithmsCovering Rule-Based) AlgorithmsClustering: An OverviewClustering Hierarchical AlgorithmsClustering Partitional AlgorithmsClustering: Large DatabasesClustering Categorical AttributesAssociation Rules: An OverviewAssociation Rules: Parallel and Distributed AlgorithmsAssociation Rules: Advanced Techniques and MeasuresSpatial Mining: Techniques and AlgorithmsReadership: An introductory data mining textbook or a technical data mining book for an upper level undergraduate or graduate level course.

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书名：	Lecture Notes in Data Mining
作者：	Berry, Michael W.; Browne, Murray
出版时间：	2006-09-01
ISBN：	9789812568021(P-ISBN) ，9789812773630(O-ISBN)
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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 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

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