Abstract
This paper introduces k-splits, an improved hierarchical algorithm based on k-means to cluster data without prior knowledge of the number of clusters. K-splits starts from a small number of clusters and uses the most significant data distribution axis to split these clusters incrementally into better fits if needed. Accuracy and speed are two main advantages of the proposed method. This research experiments on six synthetic benchmark datasets plus two real-world datasets MNIST and Fashion-MNIST, to show that the proposed algorithm has excellent accuracy in automatically finding the correct number of clusters under different conditions. The experimental analysis also indicates that k-splits is faster than similar methods and can even be faster than the standard k-means in lower dimensions. Furthermore, this article delves deeper into the effects of algorithm hyperparameters and dataset parameters on k-splits. Finally, it suggests using k-splits to uncover the exact position of centroids and then input them as initial points to the k-means algorithm to fine-tune the results.
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Mohammadi, S.O., Kalhor, A., Bodaghi, H. (2022). K-Splits: Improved K-Means Clustering Algorithm to Automatically Detect the Number of Clusters. In: Pandian, A.P., Fernando, X., Haoxiang, W. (eds) Computer Networks, Big Data and IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 117. Springer, Singapore. https://doi.org/10.1007/978-981-19-0898-9_15
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