Journal article
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & knowledge engineering, Vol.63(3), pp.879-893
2007
DOI: 10.1016/j.datak.2007.05.005
Abstract
A variety of cluster analysis techniques exist to group objects having similar characteristics. However, the implementation of many of these techniques is challenging due to the fact that much of the data contained in today’s databases is categorical in nature. While there have been recent advances in algorithms for clustering categorical data, some are unable to handle uncertainty in the clustering process while others have stability issues. This research proposes a new algorithm for clustering categorical data, termed Min–Min-Roughness (MMR), based on Rough Set Theory (RST), which has the ability to handle the uncertainty in the clustering process.
Details
- Title: Subtitle
- MMR: An algorithm for clustering categorical data using Rough Set Theory
- Creators
- Darshit Parmar - Arizona State UniversityTeresa Wu - Arizona State UniversityJennifer Blackhurst - Arizona State University
- Resource Type
- Journal article
- Publication Details
- Data & knowledge engineering, Vol.63(3), pp.879-893
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.datak.2007.05.005
- ISSN
- 0169-023X
- eISSN
- 1872-6933
- Language
- English
- Date published
- 2007
- Academic Unit
- Business Analytics; Bus Admin Graduate Programs
- Record Identifier
- 9984201429102771
Metrics
17 Record Views