On clustering attribute-oriented induction

Muyeba, M.; Khan, M.S.; Gong, Z.

Abstract

Conceptual clustering forms groups of related data items using some distance metrics. Inductive techniques like attribute-oriented induction AOI) generate meta-level descriptions of attribute values without explicitly stated distance metrics and overall goodness functions required for a clustering algorithm. The generalisation process in AOI, per attribute basis, groups attribute values using concise descriptions of a tree hierarchy for that attribute. A conceptual clustering approach is considered for attribute-oriented induction where goodness functions for maintaining intra-cluster tightness within clusters, inter-cluster dissimilarity between clusters and cluster quality evaluation are defined. Attributes are partitioned into natural common parent concept clusters, their tightness, dissimilarity and quality computed for determining a cluster to generalise within the chosen attribute. This principle minimises overgeneralisation and follows a natural clustering approach. Overall, AOI is presented as an agglomerative clustering algorithm, clusterAOI and comparative effectiveness with classical AOI analysed.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
• Start Date: Dec 11, 2006
• Publication Date: 2007
• Deposit Date: Apr 11, 2025
• Peer Reviewed: Peer Reviewed
• ISBN: ISBN 978-1-84628-662-
• DOI: https://doi.org/10.1007/978-1-84628-663-6_32