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Low-complexity fuzzy relational clustering algorithms for Web mining
Abstract
This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (RFCMdd)-for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis.
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Cited by
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Metadata
- Authors
- Krishnapuram, R., Joshi, A., Nasraoui, O., & Yi, L.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 2001-08
- Volume
- 9
- Issue
- 4
- Pages
- 595-607
- ISSN
- 1063-6706 (ISSN-L)
- 1941-0034
- OpenAlex
- W2136753665
- Cited by
- 392
- References
- 0
- FWCI
- 10.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Low-complexity fuzzy relational clustering algorithms for Web mining. https://app.cochat.ai/papers/10.1109/91.940971
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- Krishnapuram, R., Joshi, A., Nasraoui, O., & Yi, L. (2001). Low-complexity fuzzy relational clustering algorithms for Web mining. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/91.940971
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