Abstract
With the rapid proliferation of data across every stream makes raw data practically unusable. In this scenario, clustering has a major impact in grouping similar data into a dataset. This enhances the usability and meaningfulness of data, and further, the quantitative analysis can also be performed. In our existing research, a novel Probabilistic Possibilistic Fuzzy C-Means (PPFCM) clustering method is proposed. In this paper, the proposed PPFCM clustering technique is quantitatively evaluated based on several metrics and the accuracy of the clustering outcome as well as the execution output are investigated. A comparative study is made with the proposed PPFCM clustering with the traditional clustering methods, and the results are plotted. In this work, six benchmark datasets based on different application is used for evaluating the performance of PPFCM clustering method. To measure the productivity of the proposed clustering technique the Sum of Square Error (SSE) metric is used and it is found that the methodology mentioned above performs well for segmentation.
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Vijaya, J., Syed, H. (2021). A Performance Study of Probabilistic Possibilistic Fuzzy C-Means Clustering Algorithm. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_39
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