A Propound Method for the Improvement of Cluster Quality
Shveta Kundra Bhatia, V.S. Dixit
TL;DR
This paper introduces the Knockout Refinement Algorithm (KRA) to enhance cluster quality by refining initial clusters from SOM and K-Means, demonstrating improved metrics in educational data.
Contribution
The paper presents a novel refinement algorithm (KRA) based on contingency tables that improves clustering quality over original methods.
Findings
KRA improves cluster quality metrics.
Refined clusters outperform original clusters in tests.
Effective in educational data domain.
Abstract
In this paper Knockout Refinement Algorithm (KRA) is proposed to refine original clusters obtained by applying SOM and K-Means clustering algorithms. KRA Algorithm is based on Contingency Table concepts. Metrics are computed for the Original and Refined Clusters. Quality of Original and Refined Clusters are compared in terms of metrics. The proposed algorithm (KRA) is tested in the educational domain and results show that it generates better quality clusters in terms of improved metric values.
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Taxonomy
TopicsAdvanced Clustering Algorithms Research · Data Management and Algorithms · Data Mining Algorithms and Applications
MethodsSelf-Organizing Map · k-Means Clustering