Implementation of fuzzy possibilistic product partition C-means and modified fuzzy possibilistic C-means clustering to pick the low performers using R-tool

1Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The different techniques like clustering, classification, association rule and regression are available in data mining to deal with a huge number of datasets that are available in the education field. The main purpose of educational data mining is to extract useful information that will create a good impact on educational institutions. The identification of risk students, improving the graduation rates and placement opportunities will assess the institutional performance. The clustering is one of the famous techniques to deal with noisy and disjoint groups. The clustering technique is used to measure the distance between data objects of a similar group and also it finds the different cluster center in each iteration. The placement creates the opportunity to learn specific skills on their subject or industry and improves their knowledge in various sectors. In this paper, we are going to discuss Fuzzy Possibilistic Product Partition C-Means (FPPPCM) and Modified Fuzzy Possibilistic C-Means Clustering (MFPCM) performance while dealing with the student placement performance details. The improvement of the educational system will depend on reducing the low performing students rate. The main aim of this paper to pick the low performers by using FPPPCM and MFPCM algorithms. This will helps academia to identify the low performers and provide proper training to them in an early stage. And also the efficiency of FPPPCM and MFPCM is going to analyze with different parameters.

References Powered by Scopus

Top 10 algorithms in data mining

4389Citations
N/AReaders
Get full text

A survey of clustering data mining techniques

1407Citations
N/AReaders
Get full text

A possibilistic fuzzy c-means clustering algorithm

1139Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Performance Comparison of Dimensional Reduction using Principal Component Analysis with Alternating Least Squares in Modified Fuzzy Possibilistic C-Means and Fuzzy Possibilistic C-Means

0Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Thilagaraj, T., & Sengottaiyan, N. (2019). Implementation of fuzzy possibilistic product partition C-means and modified fuzzy possibilistic C-means clustering to pick the low performers using R-tool. International Journal of Recent Technology and Engineering, 8(2), 5942–5946. https://doi.org/10.35940/ijrte.B3580.078219

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 2

40%

Professor / Associate Prof. 1

20%

Lecturer / Post doc 1

20%

Researcher 1

20%

Readers' Discipline

Tooltip

Computer Science 3

60%

Social Sciences 1

20%

Engineering 1

20%

Save time finding and organizing research with Mendeley

Sign up for free