In order to solve the local convergence problem of the Cross Entropy Clustering algorithm, a split-and-merge operation is introduced to escape from local minima and reach a better solution. We describe the theoretical aspects of the method in a limited space, present a few strategies of tweaking the clustering algorithm and compare them with existing solutions. The experiments show that the presented approach increases flexibility and effectiveness of the whole algorithm.
CITATION STYLE
Hajto, K., Kamieniecki, K., Misztal, K., & Spurek, P. (2017). Split-and-merge tweak in cross entropy clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10244 LNCS, pp. 193–204). Springer Verlag. https://doi.org/10.1007/978-3-319-59105-6_17
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