An improved exponential metric space approach for C‐mean clustering analysing

In this article, we present two resilient algorithms, the improved alternative hard c‐means (IAHCM) and the improved alternative fuzzy c‐means (IAFCM). We implement the Gaussian distance‐dependent function proposed by Zhang and Chen (D.‐Q. Zhang and Chen, 2004). In some cases, Zhang and Chen's...

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Bibliographic Details
Published in:Expert systems 2024-05, Vol.41 (5), p.n/a
Main Authors: Kumar, Rakesh, Joshi, Varun, Dhiman, Gaurav, Viriyasitavat, Wattana
Format: Article
Language:English
Subjects:
Algorithms
alternative fuzzy c‐mean
Centroids
Cluster analysis
Clustering
fuzzy c‐mean alternative
hard c‐means
Metric space
noise clustering
robust clustering
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Summary:In this article, we present two resilient algorithms, the improved alternative hard c‐means (IAHCM) and the improved alternative fuzzy c‐means (IAFCM). We implement the Gaussian distance‐dependent function proposed by Zhang and Chen (D.‐Q. Zhang and Chen, 2004). In some cases, Zhang and Chen's metric distance does not account for the clustering centroid effect predicted by the large value. R* is employed in IAHCM and IAFCM to discover robust results while minimizing its sensitivity. Experiments are conducted using two‐and three‐dimensional data, including Diamond and Iris real‐world data. The results are based on demonstrating the robust simplicity and applicability of the offered algorithms. Similarly, computational complexity is assessed.
ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.12896