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Computer Science - Security and Cryptology | Knowledge Discovery in Inductive Databases - 5th International Workshop, KDID 2006 Berlin, Germany,

Knowledge Discovery in Inductive Databases

5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers

Dzeroski, Saso, Struyf, Jan (Eds.)

2007, X, 301 p.

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This book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in Berlin, Germany, September 2006 in association with ECML/PKDD.

The 15 revised full papers presented together with one invited paper were carefully selected during two rounds of reviewing and improvement for inclusion in the book. Bringing together the fields of databases, machine learning, and data mining the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.

Content Level » Research

Keywords » Pattern Mining - classification - clustering - constraint-based mining - data management - data mining - database - inductive databases - knowledge discovery - learning - machine learning - multi-objective regression - query languages - query optimization - relational database

Related subjects » Artificial Intelligence - Database Management & Information Retrieval - Security and Cryptology

Table of contents 

Invited Talk.- Value, Cost, and Sharing: Open Issues in Constrained Clustering.- Contributed Papers.- Mining Bi-sets in Numerical Data.- Extending the Soft Constraint Based Mining Paradigm.- On Interactive Pattern Mining from Relational Databases.- Analysis of Time Series Data with Predictive Clustering Trees.- Integrating Decision Tree Learning into Inductive Databases.- Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets.- An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results.- Beam Search Induction and Similarity Constraints for Predictive Clustering Trees.- Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs.- Extracting Trees of Quantitative Serial Episodes.- IQL: A Proposal for an Inductive Query Language.- Mining Correct Properties in Incomplete Databases.- Efficient Mining Under Rich Constraints Derived from Various Datasets.- Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth.- Discussion Paper.- Towards a General Framework for Data Mining.

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