Abstract
The hard and soft clustering techniques have membership functions. The chief objective of these functions is converging the final solution at the optimum global location. This chapter discusses the existing quantitative analysis methods to demonstrate the segmentation performance of clustering techniques. In the earliest quantitative analysis methods of clustering techniques, the MSE (mean square error), inter-cluster variation, and VXB function have generally been using that measure the local cluster similarity only. As compared to the former quantitative analysis methods, the three modern methods measure the local cluster similarity and the global homogeneity of segmented images without any human interaction or predefined threshold settings. This chapter discusses the working and the practical knowledge of the existing evaluation methods.
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Siddiqui, F.U., Yahya, A. (2022). Quantitative Analysis Methods of Clustering Techniques. In: Clustering Techniques for Image Segmentation. Springer, Cham. https://doi.org/10.1007/978-3-030-81230-0_4
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DOI: https://doi.org/10.1007/978-3-030-81230-0_4
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