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Knowledge Discovery in Inductive Databases

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Cover of 'Knowledge Discovery in Inductive Databases'

Table of Contents

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    Book Overview
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    Chapter 1 Value, Cost, and Sharing: Open Issues in Constrained Clustering
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    Chapter 2 Mining Bi-sets in Numerical Data
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    Chapter 3 Extending the Soft Constraint Based Mining Paradigm
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    Chapter 4 On Interactive Pattern Mining from Relational Databases
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    Chapter 5 Analysis of Time Series Data with Predictive Clustering Trees
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    Chapter 6 Integrating Decision Tree Learning into Inductive Databases
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    Chapter 7 Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets
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    Chapter 8 An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results
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    Chapter 9 Beam Search Induction and Similarity Constraints for Predictive Clustering Trees
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    Chapter 10 Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs
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    Chapter 11 Extracting Trees of Quantitative Serial Episodes
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    Chapter 12 IQL: A Proposal for an Inductive Query Language
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    Chapter 13 Mining Correct Properties in Incomplete Databases
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    Chapter 14 Efficient Mining Under Rich Constraints Derived from Various Datasets
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    Chapter 15 Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth
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    Chapter 16 Towards a General Framework for Data Mining
Attention for Chapter 7: Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets
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