Implementation of Fuzzy C-Means and Fuzzy Possibilistic C-Means Algorithms to Find the Low Performers using R-Tool

Data mining research continuous in the field of education by dealing with a large amount of data with more number of techniques. The clustering is a suitable technique to deal with the disjoint group of data sets by assign the objects which are similar to the corresponding group. This paper discusses the use of Fuzzy C-Means (FCM) and Fuzzy Possibilistic C-Means (FPCM) algorithms to predict low performers for placement in the software industry. The fuzzy clustering plays an active role by solving real-world tough tasks. In fuzzy clustering, the FCM algorithm is efficient, popular and it is easy to implement in different data sets. The FPCM is also having high efficiency to deal unlabeled data which may generate membership and typical values. The early prediction of low performers will support academia to analyze and provide necessary training to them on a premature stage. Here the factors like academic, aptitude, technical and interpersonal skills are needed to analyze all students to provide better placement for low performers.

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