Abstract
The Fuzzy C-Means clustering technique is one of the most popular soft clustering algorithms in the field of data segmentation. However, its high time complexity makes it computationally expensive, when implemented on very large datasets. Kolen and Hutcheson [1] proposed a modification of the FCM Algorithm, which dramatically reduces the runtime of their algorithm, making it linear with respect to the number of clusters, as opposed to the original algorithm which was quadratic with respect to the number of clusters. This paper proposes further modification of the algorithm by Kolen et al., by suggesting effective seed initialisation (by Fuzzy C-Means++, proposed by Stetco et al. [2]) before feeding the initial cluster centers to the algorithm. The resultant model converges even faster. Empirical findings are illustrated using two synthetic and two real-world datasets.
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Bhattacherjee, A., Sanyal, S., Abraham, A. (2022). Reducing Time Complexity of Fuzzy C Means Algorithm. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_33
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DOI: https://doi.org/10.1007/978-3-030-96299-9_33
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