Abstract
Detecting clusters of different sizes represents a serious difficulty for all c-means clustering models. This study investigates the set of various modified fuzzy c-means clustering algorithms within the bounds of the probabilistic constraint, from the point of view of their sensitivity to cluster sizes. Two numerical frameworks are constructed, one of them addressing clusters of different cardinalities but relatively similar diameter, while the other manipulating with both cluster cardinality and diameter. The numerical evaluations have shown the existence of algorithms that can effectively handle both cases. However, these are difficult to automatically adjust to the input data through their parameters.
The work of S.M. Szilágyi was supported by the János Bolyai Fellowship Program of the Hungarian Academy of Sciences.
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Szilágyi, L., Szilágyi, S.M., Enăchescu, C. (2016). A Study on Cluster Size Sensitivity of Fuzzy c-Means Algorithm Variants. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_53
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