Abstract
Clustering is an important methodology for data mining and data analysis. K-Means is a simple and fast algorithm for clustering data. However the performance of K-means is highly sensitive on the initial seed of the algorithm. Heuristic Kalman Algorithm (HKA) is a population based stochastic optimization technique which is an effective method for searching a near-optimal solution of a function. Although HKA has good global search characteristics, it is shown that when directly applied on clustering it performs poorly. This paper proposes a new approach KHKA, which combines the benefits of the global nature of HKA and the fast convergence of K-means. KHKA was implemented and benchmarked on synthetic and real datasets from UCI Machine Learning Repository. The results were compared with other population based, stochastic algorithms. Results show that KHKA is a promising algorithm and was able to perform better than the compared algorithms with respect to the used datasets.
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Acknowledgments
The author would like to thank Dr. Vikram Pakrashi, University College Cork, Ireland, for the valuable suggestions, discussions and constant support throughout the entire period of research.
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Pakrashi, A. (2015). A New Hybrid Clustering Approach Based on Heuristic Kalman Algorithm. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_39
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DOI: https://doi.org/10.1007/978-3-319-20294-5_39
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