Abstract
In the field of cluster analysis, most of existing algorithms assume that each feature of the samples plays a uniform contribution for cluster analysis. Feature-weight assignment is a special case of feature selection where different features are ranked according to their importance. The feature is assigned a value in the interval [0, 1] indicating the importance of that feature, we call this value "feature-weight". In this paper we propose a new feature weighted fuzzy c-means clustering algorithm in a way which this algorithm be able to obtain the importance of each feature, and then use it in appropriate assignment of feature-weight. These weights incorporated into the distance measure to shape clusters based on variability, correlation and weighted features.
| Original language | English |
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| Title of host publication | Proceedings of the IADIS European Conference on Data Mining 2009, ECDM'09 Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009 |
| Pages | 11-18 |
| Number of pages | 8 |
| Publication status | Published - 1 Dec 2009 |
| Externally published | Yes |
| Event | IADIS European Conference on Data Mining 2009, ECDM'09. Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009 - Algarve, Portugal Duration: 18 Jun 2009 → 20 Jun 2009 |
Conference
| Conference | IADIS European Conference on Data Mining 2009, ECDM'09. Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009 |
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| Country/Territory | Portugal |
| City | Algarve |
| Period | 18/06/09 → 20/06/09 |
Keywords
- Cluster analysis
- Feature weighted
- Fuzzy clustering