Autonomous clustering using rough set theory

Charlotte Bean; Chandra Kambhampati

doi:10.1007/s11633-008-0090-3

Autonomous clustering using rough set theory

Charlotte Bean, Chandra Kambhampati

Research output: Contribution to journal › Article › peer-review

33 Citations (Scopus)

Abstract

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory (RST). The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.

Original language	English
Pages (from-to)	90-102
Number of pages	13
Journal	International Journal of Automation and Computing
Volume	5
Issue number	1
DOIs	https://doi.org/10.1007/s11633-008-0090-3
Publication status	Published - 1 Jan 2008
Externally published	Yes

Keywords

Autonomous
Data clustering
Knowledge-oriented clustering
Rough set theory (RST)

ASJC Scopus subject areas

Control and Systems Engineering
Modelling and Simulation
Computer Science Applications
Applied Mathematics

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10.1007/s11633-008-0090-3

Cite this

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KW - Knowledge-oriented clustering

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Autonomous clustering using rough set theory

Abstract

Keywords

ASJC Scopus subject areas

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