ITEM METADATA RECORD
Title: Inductive querying for discovering subgroups and clusters
Authors: Zimmermann, Albrecht ×
De Raedt, Luc
Issue Date: 2004
Publisher: Springer-verlag berlin
Host Document: Constraint-based mining and inductive databases pages:380-399
Abstract: We introduce the problem of cluster-grouping and show that it integrates several important data mining tasks, i.e. subgroup discovery, mining correlated patterns and aspects from clustering. The problem of cluster-grouping can be regarded as a new type of inductive optimization query that asks for the k best patterns according to a convex criterion. The algorithm CG for solving cluster-grouping problems is presented and the underlying mechanisms are discussed. The approach is experimentally evaluated on a number of real-life data sets. The results indicate that the algorithm improves upon the subgroup discovery algorithm CN2-WRAcc and is competitive with the clustering algorithm Cob Web.
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author

Files in This Item:
File Status SizeFormat
inductive-querying.pdf Published 322KbAdobe PDFView/Open

 


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science