Abstract
The special techniques like clustering and classification exist in data mining to handle any number of datasets that are available in the education field. The main use of data mining is to take out valuable information to create new knowledge in the field of education. The detection of low performers, improving the pass percentage and employment opportunities, is the main goal of every educational institution. In data mining, the well-known technique is to deal with disjoint and noisy data is clustering. This technique used for distance calculation between similar group objects and the different cluster centers is also found. In this paper, the implementation of fuzzy models like Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), Modified Fuzzy Possibilistic C-Means (MFPCM) and Fuzzy Possibilistic Product Partition C-Means (FPPPCM) clustering algorithms is used to measure the student’s levels and low performers identification through its size.
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Thilagaraj, T., Sengottaiyan, N. (2020). Implementation of Fuzzy Clustering Algorithms to Analyze Students Performance Using R-Tool. In: Peng, SL., Son, L.H., Suseendran, G., Balaganesh, D. (eds) Intelligent Computing and Innovation on Data Science. Lecture Notes in Networks and Systems, vol 118. Springer, Singapore. https://doi.org/10.1007/978-981-15-3284-9_31
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DOI: https://doi.org/10.1007/978-981-15-3284-9_31
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