Affiliations: [a] Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, Malaysia | [b] Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia | [c] Department of Mathematics, Faculty of Science, Adamawa State University, Mubi, Nigeria
Correspondence:
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Corresponding author: Paul Inuwa Dalatu, Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, Malaysia, and Department of Mathematics, Faculty of Science, Adamawa State University, Mubi, Nigeria. E-mail: dalatup@gmail.com.
Abstract: In recent years, the study of distance functions has been speedily developing, this motivated us to propose and improve former distance measure techniques. In traditional distance functions research, much has been done by many researchers in determining the similarity attributes of dataset; but few has attempted to combine two or more distance functions to enhance the accuracy, effectiveness, and efficiency in evaluating the performance of either the external or internal validity measures in K-Means clustering algorithms. Therefore, the paper proposes an improved approach to distance functions using K-Means clustering. We experimented with standard datasets from the UCI machine learning source and it was observed that the proposed approach performed better when compared to the traditional distance functions as shown by all the external validity measures results.
