Analysis of Placement for Electronics and Communication Engineering Students using Multiple Clustering

Abstract

Inspired by the success of supervised bagging and boosting algorithms, we propose non-adaptive and adaptive re-sampling schemes for the integration of multiple independent and dependent clustering. We investigate the effectiveness of bagging techniques, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms. In our adaptive approach, individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given data set. The sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. New subsamples are then drawn to increasingly focus on the problematic regions of the input feature space. The comparison of adaptive and non-adaptive approaches is a new avenue for research, and this study helps to pave the way for the useful application of distributed data mining methods.