TY - JOUR ID - 22235 TI - A new validity index for fuzzy-possibilistic c-means clustering JO - Scientia Iranica JA - SCI LA - en SN - 1026-3098 AU - Fazel Zarandi, Mohammad Hossein AU - Sotodian, Shahabeddin AU - Castillo, Oscar AD - Department of Industrial Engineering and Management Systems,Amirkabir University of Technology,Tehran,Iran AD - Tijuana Institutes of Technology,Tijuana,Mexico Y1 - 2021 PY - 2021 VL - 28 IS - 4 SP - 2277 EP - 2293 KW - Fuzzy-Possibilistic clustering KW - Cluster validity index KW - Exponential separation KW - Medical pattern recognition KW - Microarray gene expression DO - 10.24200/sci.2021.50287.1614 N2 - In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining the optimal number of clusters. This paper presents a new validity index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic)FP (index, which works well in the presence of clusters that vary in shape and density. Moreover, FPCM like most of the clustering algorithms is susceptible to some initial parameters. In this regard, in addition to the number of clusters, FPCM requires a priori selection of the degree of fuzziness (m) and the degree of typicality (η). Therefore, we presented an efficient procedure for determining an optimal value for and . The proposed approach has been evaluated using several synthetic and real-world datasets. Final computational results demonstrate the capabilities and reliability of the proposed approach compared with several well-known fuzzy validity indices in the literature. Furthermore, to clarify the ability of the proposed method in real applications, the proposed method is implemented in microarray gene expression data clustering and medical image segmentation. UR - https://scientiairanica.sharif.edu/article_22235.html L1 - https://scientiairanica.sharif.edu/article_22235_f027f8a75d52e0c354fe7e1458f63db0.pdf ER -