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Principles of Data Mining and Knowledge Discovery

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Cover of 'Principles of Data Mining and Knowledge Discovery'

Table of Contents

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    Book Overview
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    Chapter 1 Optimized Substructure Discovery for Semi-structured Data
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    Chapter 2 Fast Outlier Detection in High Dimensional Spaces
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    Chapter 3 Data Mining in Schizophrenia Research — Preliminary Analysis
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    Chapter 4 Fast Algorithms for Mining Emerging Patterns
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    Chapter 5 On the Discovery of Weak Periodicities in Large Time Series
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    Chapter 6 The Need for Low Bias Algorithms in Classification Learning from Large Data Sets
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    Chapter 7 Mining All Non-derivable Frequent Itemsets
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    Chapter 8 Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance
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    Chapter 9 Finding Association Rules with Some Very Frequent Attributes
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    Chapter 10 Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*
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    Chapter 11 A Classification Approach for Prediction of Target Events in Temporal Sequences
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    Chapter 12 Privacy-Oriented Data Mining by Proof Checking
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    Chapter 13 Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification
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    Chapter 14 Generating Actionable Knowledge by Expert-Guided Subgroup Discovery
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    Chapter 15 Clustering Transactional Data
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    Chapter 16 Multiscale Comparison of Temporal Patterns in Time-Series Medical Databases
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    Chapter 17 Association Rules for Expressing Gradual Dependencies
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    Chapter 18 Support Approximations Using Bonferroni-Type Inequalities
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    Chapter 19 Using Condensed Representations for Interactive Association Rule Mining
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    Chapter 20 Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting
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    Chapter 21 Dependency Detection in MobiMine and Random Matrices
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    Chapter 22 Long-Term Learning for Web Search Engines
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    Chapter 23 Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database
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    Chapter 24 Involving Aggregate Functions in Multi-relational Search
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    Chapter 25 Information Extraction in Structured Documents Using Tree Automata Induction
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    Chapter 26 Algebraic Techniques for Analysis of Large Discrete-Valued Datasets
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    Chapter 27 Geography of Di.erences between Two Classes of Data
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    Chapter 28 Rule Induction for Classification of Gene Expression Array Data
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    Chapter 29 Clustering Ontology-Based Metadata in the Semantic Web
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    Chapter 30 Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases
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    Chapter 31 SVM Classification Using Sequences of Phonemes and Syllables
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    Chapter 32 A Novel Web Text Mining Method Using the Discrete Cosine Transform
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    Chapter 33 A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases
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    Chapter 34 Answering the Most Correlated N Association Rules Efficiently
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    Chapter 35 Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model
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    Chapter 36 Efficiently Mining Approximate Models of Associations in Evolving Databases
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    Chapter 37 Explaining Predictions from a Neural Network Ensemble One at a Time
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    Chapter 38 Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD
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    Chapter 39 Separability Index in Supervised Learning
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    Chapter 40 Finding Hidden Factors Using Independent Component Analysis
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    Chapter 41 Reasoning with Classifiers*
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    Chapter 42 A Kernel Approach for Learning from Almost Orthogonal Patterns
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    Chapter 43 Learning with Mixture Models: Concepts and Applications
Attention for Chapter 29: Clustering Ontology-Based Metadata in the Semantic Web
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Citations

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Chapter title
Clustering Ontology-Based Metadata in the Semantic Web
Chapter number 29
Book title
Principles of Data Mining and Knowledge Discovery
Published in
Lecture notes in computer science, September 2002
DOI 10.1007/3-540-45681-3_29
Book ISBNs
978-3-54-044037-6, 978-3-54-045681-0
Authors

Alexander Maedche, Valentin Zacharias

Editors

Tapio Elomaa, Heikki Mannila, Hannu Toivonen

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 6 5%
United States 4 3%
Portugal 2 2%
Spain 2 2%
Turkey 1 <1%
France 1 <1%
China 1 <1%
Belgium 1 <1%
Australia 1 <1%
Other 4 3%
Unknown 107 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 27%
Researcher 26 20%
Student > Master 19 15%
Professor > Associate Professor 8 6%
Student > Doctoral Student 7 5%
Other 27 21%
Unknown 8 6%
Readers by discipline Count As %
Computer Science 90 69%
Engineering 9 7%
Business, Management and Accounting 6 5%
Social Sciences 4 3%
Agricultural and Biological Sciences 2 2%
Other 8 6%
Unknown 11 8%