Xu G., Zong Y., Yang Z. Applied Data Mining

Boca Raton: CRC Press, 2013. — 284 p. — ISBN: 1466585838, 9781466513709Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.Fundamentals
IntroductionOrganization of the Book
The Audience of the Book
Mathematical Foundations
Organization of Data
Data Distribution
Distance Measures
Similarity Measures
Dimensionality ReductionData Preparation
Attribute Selection
Data Cleaning and Integrity
Multiple Model IntegrationClustering Analysis
Clustering Analysis
Types of Data in Clustering Analysis
Traditional Clustering Algorithms
High-dimensional clustering algorithm
Constraint-based Clustering Algorithm
Consensus Clustering AlgorithmClassification
Classification Definition and Related Issues
Decision Tree and Classification
Bayesian Network and ClassificationFrequent Pattern Mining
Association Rule Mining
Sequential Pattern Mining
Frequent Subtree Mining
Frequent Subgraph MiningAdvanced Data Mining
Advanced Clustering AnalysisSpace Smoothing Search Methods in Heuristic Clustering
Using Approximate Backbone for Initializations in Clustering
Improving Clustering Quality in High Dimensional SpaceMulti-Label ClassificationWhat is Multi-label Classification
Problem Transformation
Algorithm Adaptation
Evaluation Metrics and DatasetsPrivacy Preserving in Data Mining
The K-Anonymity Method
The l-Diversity Method
The t-Closeness Method
Discussion and ChallengesEmerging Applications
Data Stream
General Data Stream Models
Sampling Approach
Wavelet Method
Sketch Method
Histogram Method
DiscussionRecommendation Systems
Collaborative Filtering
PLSA Method
Tensor Model
Discussion and ChallengesSocial Tagging Systems
Data Mining and Information Retrieval
Recommender Systems
Clustering Algorithms in Recommendation
Clustering Algorithms in Tag-Based Recommender Systems