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Improved fuzzy [formula omitted]-means clustering by varying the fuzziness parameter.
- Source :
-
Pattern Recognition Letters . May2022, Vol. 157, p60-66. 7p. - Publication Year :
- 2022
-
Abstract
- • Clustering results of vFCM are more closer to the minimum of the k means objective. • vFCM provides reduced sensitivity to initialization. • vFCM improves the clustering performance in high dimensions. • vFCM does not need careful tuning as FCM. • Experimental results of vFCM are similar to or better than k means and FCM. Fuzzy c -means (FCM) is one of the most frequently used methods for clustering, where the fuzziness weighting exponent m is a key hyper-parameter that directly affects the clustering performance. However, FCM requires careful tuning the fuzziness parameter which results in significant time costs. In this research, an improved FCM clustering by v arying the fuzziness parameter, called vFCM, is proposed to overcome this issue, based on the facts that the FCM objective is easy to optimize when m is large, while more local valleys appear as m decreases, hence the optimization problem presents a search process from simple to complex when m varies from a large value to a small value approaching 1. Here, the nature of m is similar to the temperature parameter in the deterministic annealing, and moving along a sequence of the FCM objectives by a linear method that proposes to update m automatically provides a form of annealing. Extensive experiments on simulated and real-world data sets show that vFCM is not only more robust to initialization but also improves the clustering performance in high dimensions. Furthermore, the clustering results of vFCM have a low fluctuation according to different m , so it does not require careful tuning the fuzziness parameter. The time that vFCM takes is greatly reduced. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FUZZY algorithms
*EVOLUTIONARY algorithms
*EXPONENTS
*VALLEYS
*TEMPERATURE
*COST
Subjects
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 157
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 156520799
- Full Text :
- https://doi.org/10.1016/j.patrec.2022.03.017