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.
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© 2007 Springer-Verlag London Limited
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Muyeba, M., Khan, M.S., Gong, Z. (2007). On Clustering Attribute-oriented Induction. In: Bramer, M., Coenen, F., Tuson, A. (eds) Research and Development in Intelligent Systems XXIII. SGAI 2006. Springer, London. https://doi.org/10.1007/978-1-84628-663-6_32
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DOI: https://doi.org/10.1007/978-1-84628-663-6_32
Publisher Name: Springer, London
Print ISBN: 978-1-84628-662-9
Online ISBN: 978-1-84628-663-6
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