Abstract
Most clustering algorithms become ineffective when provided with unsuitable parameters or applied to data-sets which are composed of clusters with diverse shapes, sizes, and densities.
In our paper we present a new version of k-means method, that allows adding one new cluster to the k cluster we already had with out retraining from scratch. This method is based on the splitting process, we are looking for the cluster that had the highest score to be split, our score is based on three criteria; SSE, Dispersion-index and the size of cluster. Finally, the split process is performed by using standard K. Experimental results demonstrate the effectiveness of our approach both on simulated and real data-sets.
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Ounali, C., Ben Rejab, F., Nouira Ferchichi, K. (2020). Incremental Algorithm Based on Split Technique. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_55
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DOI: https://doi.org/10.1007/978-3-030-16660-1_55
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