Abstract
Cluster forests is a novel approach for ensemble clustering based on the aggregation of partial K-means clustering trees. Cluster forests was inspired from random forests algorithm. Cluster forests gives better results than other popular clustering algorithms on most standard benchmarks. In this paper, we propose an improved version of cluster forests using fuzzy C-means clustering. Results shows that the proposed Fuzzy Cluster Forests system gives better clustering results than cluster forests for eight standard clustering benchmarks from UC Irvine Machine Learning Repository.
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Acknowledgment
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Ben Ayed, A., Ben Halima, M., Alimi, A.M. (2017). Cluster Forests Based Fuzzy C-Means for Data Clustering. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_55
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DOI: https://doi.org/10.1007/978-3-319-47364-2_55
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