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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 Self-Similar Layered Hidden Markov Models
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    Chapter 2 Automatic Text Summarization Using Unsupervised and Semi-supervised Learning
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    Chapter 3 Detecting Temporal Change in Event Sequences: An Application to Demographic Data
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    Chapter 4 Principles of Data Mining and Knowledge Discovery
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    Chapter 5 Computing Association Rules Using Partial Totals
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    Chapter 6 Gaphyl: A Genetic Algorithms Approach to Cladistics
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    Chapter 7 Parametric Approximation Algorithms for High-Dimensional Euclidean Similarity
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    Chapter 8 Data Structures for Minimization of Total Within-Group Distance for Spatio-temporal Clustering
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    Chapter 9 Non-crisp Clustering by Fast, Convergent, and Robust Algorithms
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    Chapter 10 Pattern Extraction for Time Series Classification
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    Chapter 11 Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study
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    Chapter 12 Interesting Fuzzy Association Rules in Quantitative Databases
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    Chapter 13 Interestingness Measures for Fuzzy Association Rules
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    Chapter 14 A Data Set Oriented Approach for Clustering Algorithm Selection
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    Chapter 15 Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection
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    Chapter 16 Discovery of Temporal Patterns
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    Chapter 17 Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB
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    Chapter 18 Using Grammatical Inference to Automate Information Extraction from the Web
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    Chapter 19 Biological Sequence Data Mining
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    Chapter 20 Implication-Based Fuzzy Association Rules
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    Chapter 21 A General Measure of Rule Interestingness
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    Chapter 22 Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization
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    Chapter 23 Propositionalisation and Aggregates
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    Chapter 24 Algorithms for the Construction of Concept Lattices and Their Diagram Graphs
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    Chapter 25 Data Reduction Using Multiple Models Integration
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    Chapter 26 Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution
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    Chapter 27 Sentence Filtering for Information Extraction in Genomics, a Classification Problem
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    Chapter 28 Text Categorization and Semantic Browsing with Self-Organizing Maps on Non-euclidean Spaces
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    Chapter 29 A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory
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    Chapter 30 Internet Document Filtering Using Fourier Domain Scoring
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    Chapter 31 Distinguishing Natural Language Processes on the Basis of fMRI-Measured Brain Activation
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    Chapter 32 Automatic Construction and Refinement of a Class Hierarchy over Multi-valued Data
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    Chapter 33 Comparison of Three Objective Functions for Conceptual Clustering
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    Chapter 34 Identification of ECG Arrhythmias Using Phase Space Reconstruction
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    Chapter 35 Finding Association Rules That Trade Support Optimally against Confidence
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    Chapter 36 Bloomy Decision Tree for Multi-objective Classification
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    Chapter 37 Discovery of Temporal Knowledge in Medical Time-Series Databases Using Moving Average, Multiscale Matching, and Rule Induction
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    Chapter 38 Mining Positive and Negative Knowledge in Clinical Databases Based on Rough Set Model
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    Chapter 39 The TwoKey Plot for Multiple Association Rules Control
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    Chapter 40 Lightweight Collaborative Filtering Method for Binary-Encoded Data
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    Chapter 41 Support Vectors for Reinforcement Learning
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    Chapter 42 Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining
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    Chapter 43 Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining
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    Chapter 44 The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery
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    Chapter 45 Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery
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Citations

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Readers on

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19 Mendeley
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Title
Principles of Data Mining and Knowledge Discovery
Published by
Springer Science & Business Media, August 2001
DOI 10.1007/3-540-44794-6
ISBNs
978-3-54-042534-2, 978-3-54-044794-8
Editors

Raedt, Luc de, Siebes, Arno

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 11%
Professor 1 5%
Unknown 16 84%
Readers by discipline Count As %
Engineering 2 11%
Computer Science 1 5%
Unknown 16 84%