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A novel FCM clustering algorithm for interval-valued data and fuzzy-valued data
- Source :
- WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.
- Publication Year :
- 2002
- Publisher :
- IEEE, 2002.
-
Abstract
- It is well known that fuzzy c-means (FCM) algorithm is one of the most popular methods of cluster analysis. However, the traditional FCM algorithm does not work for the interval-valued data and fuzzy-valued data. To this end, a feature mapping method is proposed to preprocess these special type data, and then the traditional FCM algorithm can also be employed to analyze the interval-valued and fuzzy-valued data. Therefore, a novel FCM clustering algorithm is formed for interval-valued data and fuzzy-valued data. The experimental result demonstrates its effectiveness.
- Subjects :
- Clustering high-dimensional data
Fuzzy clustering
Mathematics::General Mathematics
business.industry
Correlation clustering
Pattern recognition
computer.software_genre
ComputingMethodologies_PATTERNRECOGNITION
Data stream clustering
Computer Science::Computational Engineering, Finance, and Science
CURE data clustering algorithm
Computer Science::Computer Vision and Pattern Recognition
Canopy clustering algorithm
Artificial intelligence
Data mining
Cluster analysis
business
Algorithm
computer
Mathematics
FSA-Red Algorithm
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000
- Accession number :
- edsair.doi...........74710b0dbedac39a77bf1ea8dbc6daa9
- Full Text :
- https://doi.org/10.1109/icosp.2000.893395