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Article: A fuzzy k-modes algorithm for clustering categorical data
| Title | A fuzzy k-modes algorithm for clustering categorical data |
|---|---|
| Authors | |
| Keywords | Categorical data Clustering Data mining Fuzzy partitioning k-means algorithm |
| Issue Date | 1999 |
| Publisher | IEEE. |
| Citation | IEEE Transactions on Fuzzy Systems, 1999, v. 7 n. 4, p. 446-452 How to Cite? |
| Abstract | This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results. |
| Persistent Identifier | http://hdl.handle.net/10722/42992 |
| ISSN | 2023 Impact Factor: 10.7 2023 SCImago Journal Rankings: 4.204 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Huang, Z | en_HK |
| dc.contributor.author | Ng, MK | en_HK |
| dc.date.accessioned | 2007-03-23T04:36:24Z | - |
| dc.date.available | 2007-03-23T04:36:24Z | - |
| dc.date.issued | 1999 | en_HK |
| dc.identifier.citation | IEEE Transactions on Fuzzy Systems, 1999, v. 7 n. 4, p. 446-452 | en_HK |
| dc.identifier.issn | 1063-6706 | en_HK |
| dc.identifier.uri | http://hdl.handle.net/10722/42992 | - |
| dc.description.abstract | This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results. | en_HK |
| dc.format.extent | 164105 bytes | - |
| dc.format.extent | 26112 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/msword | - |
| dc.language | eng | en_HK |
| dc.publisher | IEEE. | en_HK |
| dc.relation.ispartof | IEEE Transactions on Fuzzy Systems | - |
| dc.rights | ©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
| dc.subject | Categorical data | - |
| dc.subject | Clustering | - |
| dc.subject | Data mining | - |
| dc.subject | Fuzzy partitioning | - |
| dc.subject | k-means algorithm | - |
| dc.title | A fuzzy k-modes algorithm for clustering categorical data | en_HK |
| dc.type | Article | en_HK |
| dc.description.nature | published_or_final_version | en_HK |
| dc.identifier.doi | 10.1109/91.784206 | en_HK |
| dc.identifier.scopus | eid_2-s2.0-0032595161 | - |
| dc.identifier.hkuros | 52935 | - |
| dc.identifier.isi | WOS:000082167900006 | - |
| dc.identifier.issnl | 1063-6706 | - |