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Maxmin distance sort heuristic-based initial centroid method of partitional clustering for big data mining

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Abstract

The revolution of digital and communication technologies is producing an enormous amount of data. Therefore, the nature of classical data changes into big data, and mining techniques have to face high computation cost, performance and scalability-related challenges. The K-means (KM) algorithm is the most widely used partitional clustering approach that depends on K clusters, initial centroid, distance measures and central tendency statistical approaches. The initial centroid determines the computational effectiveness, efficiency and local optima issues in big data clustering due to the gradient descent nature of the KM algorithm. The existing centroid initialization algorithm has achieved low cluster quality with high computational complexity due to iterations, distance computation, data and result comparison. To overcome these deficiencies, this paper presents the Maxmin Distance Sort Heuristic (MDSH) algorithm for big data clustering through a stratified sampling process. The performance of the MDSHKM algorithm is compared with the KM and KM++  algorithms through R square, Root-Mean-Square Standard Deviation, Davies–Bouldin score, Calinski Harabasz score, Silhouette Coefficient, Number of Iterations and CPU time validation indices using eight real datasets. The experimental evaluation shows that the MDSHKM algorithm achieves better cluster quality, computing cost, efficiency and stable convergence than the KM and KM++ algorithms.

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  1. Department of Computer Science & Applications, Dr. Hari Singh Gour Vishwavidyalaya, Sagar, Madhya Pradesh, India

    Kamlesh Kumar Pandey & Diwakar Shukla

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  1. Kamlesh Kumar Pandey
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  2. Diwakar Shukla
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Correspondence to Kamlesh Kumar Pandey.

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Pandey, K.K., Shukla, D. Maxmin distance sort heuristic-based initial centroid method of partitional clustering for big data mining. Pattern Anal Applic 25, 139–156 (2022). https://doi.org/10.1007/s10044-021-01045-0

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