Abstract
The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers. To alleviate this issue, a new approach of using an evolutionary technique known as Teaching–Learning-Based Optimization (TLBO) is used hybridized with fuzzy approach. The proposed approach is able to deal with the sensitiveness of cluster centers. Results presented are very encouraging.
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Gopala Krishna, P., Lalitha Bhaskari, D. (2016). Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 379. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2517-1_46
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DOI: https://doi.org/10.1007/978-81-322-2517-1_46
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