Abstract
Clustering is an NP-hard grouping problem and thus there are advantages of using a metaheuristic (swarm intelligence) strategy to find the near global optimal solution to it. To effectively guide the agents of the swarm in the metaheuristic strategy, a suitable cost function is needed for successful outcome. The current inquiry focuses on the use of internal validation criteria as cost functions as they achieve the dual goals of clustering which are compactness and separation. Out of the multiple internal validation criteria included in the literature, two are identified for this purpose, viz. BetaCV and Dunn index. These were used as cost functions of the swarm optimizer metaheuristic (PSO-BCV and PSO-Dunn). To demonstrate the validity of the proposed technique, it was compared with other metaheuristics differential evolution as well as the traditional swarm optimizer based on distance-based criteria (PSO). The analysis of the results obtained on clustering benchmark datasets highlighted the suitability of this approach.
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20 November 2018
Correction to: Chapter “Performance of Internal Cluster Validations Measures For Evolutionary Clustering” in: B. Iyer et al. (eds.), Computing, Communication and Signal Processing, Advances in Intelligent Systems and Computing 810, https://doi.org/10.1007/978-981-13-1513-8_32
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Nerurkar, P., Pavate, A., Shah, M., Jacob, S. (2019). Performance of Internal Cluster Validations Measures For Evolutionary Clustering. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_32
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DOI: https://doi.org/10.1007/978-981-13-1513-8_32
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