A Space Efficient Minimum Spanning Tree Approach to the Fuzzy Joint Points Clustering Algorithm

The fuzzy joint points (FJPs) method is a neighborhood-based clustering method that uses a fuzzy neighborhood relation and eliminates the need for a parameter. Even though the fuzzy neighborhood-based clustering methods are proven to be fast enough, such that tens of thousands of data can be handled...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 27; no. 6; pp. 1317 - 1322
Main Authors: Atilgan, Can, Nasibov, Efendi N.
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Clustering
Clustering algorithms
Clustering methods
fuzzy joint points (FJPs)
fuzzy neighborhood
Graph theory
Level set
Memory management
Neighborhoods
Partitioning algorithms
space efficiency
Time complexity
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:The fuzzy joint points (FJPs) method is a neighborhood-based clustering method that uses a fuzzy neighborhood relation and eliminates the need for a parameter. Even though the fuzzy neighborhood-based clustering methods are proven to be fast enough, such that tens of thousands of data can be handled under a second, the space complexity is still a limiting factor. In this study, a minimum spanning tree based reduced space FJP algorithm is proposed. The computational experiments show that the reduced space algorithm enables the method to be used for much larger data sets.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2018.2879465