Abstract—This paper introduces variable minimization with the advent of data mining techniques, particularly the cluster analysis using the K-means algorithm. The results of the simulation process validated the actual ranking of the variables in each category from the 407 records of student-respondents. A noticeable decrease of variables in determining the instructional performance of teachers in the Caraga Region, Philippines is evident in the simulation results. The variables that were clustered by the algorithm that conforms to have the lowest rank based on the actual survey was removed. Results showed that there were a total of fourteen (14) variables removed from the thirty-item survey questionnaire. In this case, it only proved that like any other data mining algorithms, the cluster analysis, particularly with the K-Means algorithm, is also effective in determining what variables in the data set to omit based on the groupings with the lowest rank it generated.
Index Terms—Clustering, instructional performance, rank-based, K-Means, variable minimization.
A. J. Delima and V. Francisco are with the College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, 8400 Philippines (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Allemar Jhone P. Delima and Virnille C. Francisco, "Rank-Based Variable Minimization Using Clustering Algorithm," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 575-580, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).