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Conference Papers Year : 2014

Evidential seed-based semi-supervised clustering

Abstract

Evidential clustering algorithms produce credal partitions that enhance the concepts of hard, fuzzy or possibilistic partitions to represent all assignments ranging from complete ignorance to total certainty. This paper introduces the first semi-supervised extension of the evidential c-means clustering algorithm that can benefit from the introduction of a small set of labeled data (or seeds). Experiments conducted on real datasets show that the introduction of seeds can lead to a significant increase in clustering accuracy compared to a traditional evi- dential clustering algorithm as well as a decrease in the number of iterations to convergence.
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Dates and versions

hal-01622695 , version 1 (26-01-2023)

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Violaine Antoine, Nicolas Labroche, Viet-Vu Vu. Evidential seed-based semi-supervised clustering. 7th International Conference on Soft Computing and Intelligent Systems (SCIS), Dec 2014, Kitakyushu, Japan. pp.706-711, ⟨10.1109/SCIS-ISIS.2014.7044676⟩. ⟨hal-01622695⟩
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