Principles of Data Mining and Knowledge Discovery

Table of Contents

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   Book Overview
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   Chapter 1 Multi-relational Data Mining, Using UML for ILP
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   Chapter 2 An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
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   Chapter 3 Basis of a Fuzzy Knowledge Discovery System
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   Chapter 4 Confirmation Rule Sets
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   Chapter 5 Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery
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   Chapter 6 Combining Multiple Models with Meta Decision Trees
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   Chapter 7 Materialized Data Mining Views
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   Chapter 8 Approximation of Frequency Queries by Means of Free-Sets
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    Chapter 9 Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control
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    Chapter 10 Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks
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    Chapter 11 Quantifying the Resilience of Inductive Classification Algorithms
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    Chapter 12 Bagging and Boosting with Dynamic Integration of Classifiers
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    Chapter 13 Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information
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    Chapter 14 Some Enhancements of Decision Tree Bagging
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    Chapter 15 Relative Unsupervised Discretization for Association Rule Mining
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    Chapter 16 Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today’s Approaches
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    Chapter 17 Unified Algorithm for Undirected Discovery of Exception Rules
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    Chapter 18 Sampling Strategies for Targeting Rare Groups from a Bank Customer Database
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    Chapter 19 Instance-Based Classification by Emerging Patterns
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    Chapter 20 Context-Based Similarity Measures for Categorical Databases
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    Chapter 21 A Mixed Similarity Measure in Near-Linear Computational Complexity for Distance-Based Methods
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    Chapter 22 Fast Feature Selection using Partial Correlation for Multi-valued Attributes
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    Chapter 23 Fast Hierarchical Clustering Based on Compressed Data and OPTICS
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    Chapter 24 Accurate Recasting of Parameter Estimation Algorithms Using Sufficient Statistics for Efficient Parallel Speed-Up
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    Chapter 25 Predictive Performance of Weighted Relative Accuracy
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    Chapter 26 Quality Scheme Assessment in the Clustering Process
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    Chapter 27 Algorithm for Matching Sets of Time Series
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    Chapter 28 MSTS: A System for Mining Sets of Time Series
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    Chapter 29 Learning First Order Logic Time Series Classifiers: Rules and Boosting
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    Chapter 30 Learning Right Sized Belief Networks by Means of a Hybrid Methodology
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    Chapter 31 Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets
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    Chapter 32 Discovering Task Neighbourhoods through Landmark Learning Performances
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    Chapter 33 Induction of Multivariate Decision Trees by Using Dipolar Criteria
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    Chapter 34 Inductive Logic Programming in Clementine
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    Chapter 35 A Genetic Algorithm-Based Solution for the Problem of Small Disjuncts
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    Chapter 36 Clustering Large, Multi-level Data Sets: An Approach Based on Kohonen Self Organizing Maps
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    Chapter 37 Trees and Induction Graphs for Multivariate Response
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    Chapter 38 CEM - A Program for Visualization and Discovery in Email
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    Chapter 39 Image Access and Data Mining: An Approach
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    Chapter 40 Decision Tree Toolkit: A Component-Based Library of Decision Tree Algorithms
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    Chapter 41 Determination of Screening Descriptors for Chemical Reaction Databases
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    Chapter 42 Prior Knowledge in Economic Applications of Data Mining
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    Chapter 43 Temporal Machine Learning for Switching Control
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    Chapter 44 Improving Dissimilarity Functions with Domain Knowledge, applications with IKBS system
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    Chapter 45 Mining Weighted Association Rules for Fuzzy Quantitative Items
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    Chapter 46 Centroid-Based Document Classification: Analysis and Experimental Results
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    Chapter 47 Applying Objective Interestingness Measures in Data Mining Systems
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    Chapter 48 Observational Logic Integrates Data Mining Based on Statistics and Neural Networks
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    Chapter 49 Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Databases
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    Chapter 50 Collective Principal Component Analysis from Distributed, Heterogeneous Data
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    Chapter 51 Hierarchical Document Clustering Based on Tolerance Rough Set Model
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    Chapter 52 Application of Data-Mining and Knowledge Discovery in Automotive Data Engineering
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    Chapter 53 Towards Knowledge Discovery from cDNA Microarray Gene Expression Data
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    Chapter 54 Mining with Cover and Extension Operators
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    Chapter 55 A User-Driven Process for Mining Association Rules
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    Chapter 56 Learning from Labeled and Unlabeled Documents: A Comparative Study on Semi-Supervised Text Classification
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    Chapter 57 Schema Mining: Finding Structural Regularity among Semistructured Data
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    Chapter 58 Improving an Association Rule Based Classifier
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    Chapter 59 Discovery of Generalized Association Rules with Multiple Minimum Supports
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    Chapter 60 Learning Dynamic Bayesian Belief Networks Using Conditional Phase-Type Distributions
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    Chapter 61 Discovering Differences in Patients with Uveitis through Typical Testors by Class
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    Chapter 62 Web Usage Mining: How to Efficiently Manage New Transactions and New Clients
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    Chapter 63 Mining Relational Databases
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    Chapter 64 Interestingness in Attribute-Oriented Induction (AOI): Multiple-Level Rule Generation
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    Chapter 65 Discovery of Characteristic Subgraph Patterns Using Relative Indexing and the Cascade Model
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    Chapter 66 Transparency and Predictive Power
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    Chapter 67 Clustering Distributed Homogeneous Datasets
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    Chapter 68 Empirical Evaluation of Feature Subset Selection Based on a Real-World Data Set
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    Chapter 69 Discovery of Ambiguous Patterns in Sequences Application to Bioinformatics
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    Chapter 70 Action-Rules: How to Increase Profit of a Company
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    Chapter 71 Aggregation and Association in Cross Tables
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    Chapter 72 An Experimental Study of Partition Quality Indices in Clustering
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    Chapter 73 Expert Constrained Clustering: A Symbolic Approach
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    Chapter 74 An Application of Association Rules Discovery to Geographic Information Systems
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    Chapter 75 Generalized Entropy and Projection Clustering of Categorical Data
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    Chapter 76 Supporting Case Acquisition and Labelling in the Context of Web Mining
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    Chapter 77 Indirect Association: Mining Higher Order Dependencies in Data
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    Chapter 78 Discovering Association Rules in Large, Dense Databases
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    Chapter 79 Providing Advice to Website Designers Towards Effective Websites Re-organization
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    Chapter 80 Clinical Knowledge Discovery in Hospital Information Systems: Two Case Studies
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    Chapter 81 Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case
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    Chapter 82 Lightweight Document Clustering
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    Chapter 83 Automatic Category Structure Generation and Categorization of Chinese Text Documents
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    Chapter 84 Mining Generalized Multiple-level Association Rules
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    Chapter 85 An Efficient Approach to Discovering Sequential Patterns in Large Databases
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    Chapter 86 Using Background Knowledge as a Bias to Control the Rule Discovery Process