Data mining tasks and methods: Clustering: conceptual clustering

Authors:
Douglas Fisher
Affiliations:
Associate Professor of Computer Science, Vanderbilt University, Nashville, Tennessee
Venue:
Handbook of data mining and knowledge discovery
Year:
2002

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Abstract

Clustering methods of machine learning place great importance on the utility of conceptual descriptions, which logically or probabilistically express patterns found in clusters. Conceptual descriptions are important for cluster interpretation, inference tasks such as pattern completion and problem solving, and for data compression, memory management, and runtime-efficiency enhancements. This article surveys a wide variety of themes and algorithms found in the clustering literature of machine learning, including the various forms of conceptual representation, inference tasks that exploit the conceptual summaries of clusters, cluster validation strategies, clustering relational data, the use of background knowledge to guide clustering, and promising scale-up strategies.