Knowledge Discovery in Inductive Databases

This book presents the thoroughly refereed joint postproceedings of the 4th International Workshop on Knowledge Discovery in Inductive Databases, October 2005. 20 revised full papers presented together with 2 are reproduced here. 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.

Inhalt

Invited Papers.- Data Mining in Inductive Databases.- Mining Databases and Data Streams with Query Languages and Rules.- Contributed Papers.- Memory-Aware Frequent k-Itemset Mining.- Constraint-Based Mining of Fault-Tolerant Patterns from Boolean Data.- Experiment Databases: A Novel Methodology for Experimental Research.- Quick Inclusion-Exclusion.- Towards Mining Frequent Queries in Star Schemes.- Inductive Databases in the Relational Model: The Data as the Bridge.- Transaction Databases, Frequent Itemsets, and Their Condensed Representations.- Multi-class Correlated Pattern Mining.- Shaping SQL-Based Frequent Pattern Mining Algorithms.- Exploiting Virtual Patterns for Automatically Pruning the Search Space.- Constraint Based Induction of Multi-objective Regression Trees.- Learning Predictive Clustering Rules.