Refik Tanju Sirmen1,*, Burak Berk Üstündağ2
CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2909-2926, 2022, DOI:10.32604/cmc.2022.023947
Abstract Unsupervised clustering and clustering validity are used as essential instruments of data analytics. Despite clustering being realized under uncertainty, validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested partitionings. Also, validity measures may be biased towards the underlying clustering method. Moreover, neglecting a confidence requirement may result in over-partitioning. In the absence of an error estimate or a confidence parameter, probable clustering errors are forwarded to the later stages of the system. Whereas, having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine learning. Herein, the validity… More >
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