K-means algorithm with a novel distance measure

Authors

SHADI ABUDALFA
MOHAMMAD MIKKI

DOI

10.3906/elk-1010-869

Abstract

In this paper, we describe an essential problem in data clustering and present some solutions for it. We investigated using distance measures other than Euclidean type for improving the performance of clustering. We also developed an improved point symmetry-based distance measure and proved its efficiency. We developed a k-means algorithm with a novel distance measure that improves the performance of the classical k-means algorithm. The proposed algorithm does not have the worst-case bound on running time that exists in many similar algorithms in the literature. Experimental results shown in this paper demonstrate the effectiveness of the proposed algorithm. We compared the proposed algorithm with the classical k-means algorithm. We presented the proposed algorithm and their performance results in detail along with avenues of future research.

Keywords

Data clustering, distance measure, point symmetry, kd-tree, k-means

First Page

1665

Last Page

1684

Recommended Citation

ABUDALFA, SHADI and MIKKI, MOHAMMAD (2013) "K-means algorithm with a novel distance measure," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 21: No. 6, Article 12. https://doi.org/10.3906/elk-1010-869
Available at: https://journals.tubitak.gov.tr/elektrik/vol21/iss6/12