Abstract
Although there are many clustering algorithms are introduced, the K-means clustering algorithm is the most universal algorithm because of its simplicity. Several modifications are also introduced to overcome the drawbacks of the K-means clustering algorithm. But still, it suffers from centroid initialization of clusters primarily which may help to elevate the efficiency of clustering. Hence this paper put forward a way of initializing cluster centroids smartly so that drawbacks of the previous algorithm may be eliminated. The proposed algorithm divides the dataspace into K (number of clusters) equally by calculating the center point of each partition set as initial cluster centers. Compared to the original and some other modified versions of the K-means grouping algorithm made by researchers, the proposed model of writing paper provides better results in terms of time complexity, space complexity, inter-cluster distance, etc.
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Hamidur Rahman, M., Begum, M. (2023). Partitional Technique for Searching Initial Cluster Centers in K-means Algorithm. In: Kaiser, M.S., Waheed, S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_22
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