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Rough clustering
Lingras, Pawan Peters, Georg
Lingras, Pawan
Peters, Georg
Author
Abstract
Traditional clustering partitions a group of objects into a number of nonoverlapping sets based on a similarity measure. In real world, the boundaries of these sets or clusters may not be clearly defined. Some of the objects may be almost equidistant from the center of multiple clusters. Traditional set theory mandates that these objects be assigned to a single cluster. Rough set theory can be used to represent the overlapping clusters. Rough sets provide more flexible representation than conventional sets, at the same time they are less descriptive than the fuzzy sets. This paper describes the basic concept of rough clustering based on k-means, genetic algorithms, Kohonen self-organizing maps, and support vector clustering. The discussion also includes a review of rough cluster validity measures, and applications of rough clustering to such diverse areas as forestry, medicine, medical imaging, web mining, super markets, and traffic engineering.
Keywords
Date
2011
Type
Journal article
Journal
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Book
Volume
1
Issue
1
Page Range
64-72
Article Number
ACU Department
School of Arts and Humanities
Faculty of Education and Arts
Faculty of Education and Arts
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DOI
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Open Access Status
License
File Access
Controlled