Iterate: A conceptual clustering algorithm for data mining

Iterate: A conceptual clustering algorithm for data mining (1998)

by Gautam Biswas , Jerry B. Weinberg , Douglas H. Fisher

Venue:	IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS
Citations:	17 - 0 self

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Datum	Value	Source
TITLE	Iterate: A conceptual clustering algorithm for data mining	INFERENCE
AUTHOR NAME	Gautam Biswas	user correction
AUTHOR NAME	Jerry B. Weinberg	user correction
AUTHOR NAME	Douglas H. Fisher	user correction
ABSTRACT	The data exploration task can be divided into three interrelated subtasks: (i) feature selection, (ii) discovery, and (iii) interpretation. This paper describes an unsupervised discovery method with biases geared toward partitioning objects into clusters that improve interpretability. The algorithm, ITERATE, employs: (i) a data ordering scheme and (ii) an iterative redistribution operator to produce maximally cohesive and distinct clusters. Cohesion or intra-class similarity is measured in terms of the match between individual objects and their assigned cluster prototype. Distinctness or inter-class dissimilarity is measured by an average of the variance of the distribution matchbetween clusters. We demonstrate that interpretability, from a problem solving viewpoint, is addressed by theintra- and interclass measures. Empirical results demonstrate the properties of the discovery algorithm, and its applications to problem solving.	user correction
YEAR	1998	INFERENCE
VENUE	IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	100--111	INFERENCE
VOLUME	28	INFERENCE
NUMBER	2	user correction
CITATIONS	42 found	ParsCit 1.0