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Fuzzy K-Means Using Non-Linear S-Distance
- Source :
- IEEE Access, Vol 7, Pp 55121-55131 (2019)
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- A considerable amount of research has been done since long to select an appropriate similarity or dissimilarity measure in cluster analysis for exposing the natural grouping in an input dataset. Still, it is an open problem. In recent years, the research community is focusing on divergence-based non-Euclidean similarity measure in partitional clustering for grouping. In this paper, the Euclidean distance of traditional Fuzzy k-means (FKM) algorithm is replaced by the S-distance, which is derived from the newly introduced S-divergence. Few imperative properties of S-distance and modified FKM are presented in this study. The performance of the proposed FKM is compared with the conventional FKM with Euclidean distance and its variants with the help of several synthetic and real-world datasets. This study focuses on how the proposed clustering algorithm performs on the adopted datasets empirically. The comparative study illustrates that the obtained results are convincing. Moreover, the achieved results denote that the modified FKM outperforms some state-of-the-art FKM algorithms.
- Subjects :
- Fuzzy K-means clustering
General Computer Science
Computer science
business.industry
General Engineering
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Similarity measure
Measure (mathematics)
Fuzzy logic
Euclidean distance
Similarity (network science)
S-divergence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
General Materials Science
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
S-distance
Cluster analysis
Divergence (statistics)
business
lcsh:TK1-9971
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....837a67df670740da5de5e99344be2023
- Full Text :
- https://doi.org/10.1109/access.2019.2910195