Rough clustering

Journal article


Lingras, Pawan and Peters, Georg. (2011) Rough clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 1(1), pp. 64 - 72. https://doi.org/10.1002/widm.16
AuthorsLingras, Pawan and Peters, Georg
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.

Year2011
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Journal citation1 (1), pp. 64 - 72
PublisherJohn Wiley and Sons Ltd
ISSN1942-4787
Digital Object Identifier (DOI)https://doi.org/10.1002/widm.16
Scopus EID2-s2.0-84864757317
Page range64 - 72
Research GroupSchool of Arts
Place of publicationUnited Kingdom
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