Unsupervised Learning Algorithm for Fuzzy Clustering
Summary :
An adaptive algorithm is presented for fuzzy clustering of data. Partitioning is fuzzified by addition of an entropy term to objective functions. The proposed method produces more convex membership functions than those given by the fuzzy c-means algorithm.
- Publication
- IEICE TRANSACTIONS on Information Vol.E76-D No.3 pp.390-391
- Publication Date
- 1993/03/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Bio-Cybernetics
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Kiichi URAHAMA, "Unsupervised Learning Algorithm for Fuzzy Clustering" in IEICE TRANSACTIONS on Information,
vol. E76-D, no. 3, pp. 390-391, March 1993, doi: .
Abstract: An adaptive algorithm is presented for fuzzy clustering of data. Partitioning is fuzzified by addition of an entropy term to objective functions. The proposed method produces more convex membership functions than those given by the fuzzy c-means algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_3_390/_p
Copy
@ARTICLE{e76-d_3_390,
author={Kiichi URAHAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Unsupervised Learning Algorithm for Fuzzy Clustering},
year={1993},
volume={E76-D},
number={3},
pages={390-391},
abstract={An adaptive algorithm is presented for fuzzy clustering of data. Partitioning is fuzzified by addition of an entropy term to objective functions. The proposed method produces more convex membership functions than those given by the fuzzy c-means algorithm.},
keywords={},
doi={},
ISSN={},
month={March},}
Copy
TY - JOUR
TI - Unsupervised Learning Algorithm for Fuzzy Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 390
EP - 391
AU - Kiichi URAHAMA
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 1993
AB - An adaptive algorithm is presented for fuzzy clustering of data. Partitioning is fuzzified by addition of an entropy term to objective functions. The proposed method produces more convex membership functions than those given by the fuzzy c-means algorithm.
ER -
English
