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.
تاريخ النشر
02/10/2013
الناشر
Turkish Journal of Electrical Engineering & Computer Sciences
شادي إبراهيم أبوضلفة | Shadi Abudalfa | 5501