A Note on Fuzzy Joint Points Clustering Methods for Large Datasets

Integrating clustering algorithms with fuzzy logic typically yields more robust methods, which require little to no supervision of user. The fuzzy joint points method is a density-based fuzzy clustering approach that can achieve quality clustering. However, early versions of the method hold high com...

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Bibliographic Details
Published inIEEE transactions on fuzzy systems Vol. 24; no. 6; pp. 1648 - 1653
Main Authors Nasibov, Efendi N., Atilgan, Can
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithm design and analysis
Algorithms
Clustering
Clustering algorithms
Clustering methods
Computer science
fuzzy clustering
fuzzy joint points (FJP) method
Fuzzy logic
Fuzzy sets
Parameters
Partitioning algorithms
Shape
Upper bound
Upper bounds
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Summary:Integrating clustering algorithms with fuzzy logic typically yields more robust methods, which require little to no supervision of user. The fuzzy joint points method is a density-based fuzzy clustering approach that can achieve quality clustering. However, early versions of the method hold high computational complexity. In a recent work, the speed of the method was significantly improved without sacrificing clustering efficiency, and an even faster but parameter-dependent method was also suggested. Yet, the clustering performance of the latter was left as an open discussion and subject of study. In this study, we prove the existence of the appropriate parameter value and give an upper bound on it to discuss whether and how the parameter-dependent method can achieve the same clustering performance with the original method.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2016.2551280