Abstract
Partitioning clustering segments images without any supervision or human interaction. The partitioning clustering process starts with a guess of a possible solution and updates the solution until there is no change in all cluster centroids. There are two different kinds of partitioning clustering techniques: (a) hard partitioning clustering and (b) fuzzy partitioning clustering. The hard partitioning clustering techniques have a hard membership function that updates the cluster-centroids using a similarity index. Often, the cluster centroid is trapped in the non-active region and becomes a dead center. Unlike the hard partitioning clustering techniques, the fuzzy partitioning clustering techniques have soft membership that is less sensitive to initialization and easily avoids dead center problems. However, the soft membership function is very sensitive to outliers. This chapter discusses the hard and fuzzy partitioning clustering techniques and illustrates the dead center, center trapping, and outlier problems by using examples. The chapter also discusses the essential variant of k-means and fuzzy c-means clustering to give an inside look at major problems related to portioning clustering.
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Siddiqui, F.U., Yahya, A. (2022). Partitioning Clustering Techniques. In: Clustering Techniques for Image Segmentation. Springer, Cham. https://doi.org/10.1007/978-3-030-81230-0_2
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DOI: https://doi.org/10.1007/978-3-030-81230-0_2
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