Abstract
In this paper, the concept of divide and federate is evaluated to find the clusters that are different in densities and shapes and are contaminated with noise. The proposed divide-and-federate clustering method is based on the density and distance evaluation of the data. Wherein, the first phase of the algorithm divides the data into different sub-clusters based on the density evaluation with respect to all the data dimensions and, in the second phase, the small sub-clusters are federated with large sub-clusters to create the actual data clusters. The federation phase of the proposed clustering method is based on the distance evaluation of clusters and is merged based on the close proximity of neighbors. The proposed clustering algorithm is capable of handling noisy data through the integration of an outlier detection preprocessing method. The usefulness of the proposed algorithm is demonstrated with some examples of complex synthetic benchmark functions.
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Rehman, A.U., Belhaouari, S.B., Stanko, T., Gorovoy, V. (2023). Divide to Federate Clustering Concept for Unsupervised Learning. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 465. Springer, Singapore. https://doi.org/10.1007/978-981-19-2397-5_3
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DOI: https://doi.org/10.1007/978-981-19-2397-5_3
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