Abstract
In this work we present a multi criteria based clustering algorithm and demonstrate its usefulness in clustering documents. The algorithm proposes various metrices to judge the veracity of the clusters formed and then finds a near optimal solution that ensures good fitness scores for the all metrices. In view of the complexity of optimizing multiple clustering goals using classical optimization techniques, the paper proposes the use of an evolutionary strategy in the form of Genetic algorithm to quickly find a near optimal cluster set that satisfies all the cluster goodness criteria. The use of Genetic algorithm also inherently allows us to overcome the problem of converging to locally optimal solutions and find a global optima. The results obtained using the proposed algorithm have been compared with the outputs from standard classical algorithms and the performances have been compared.
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Mustafi, D., Sahoo, G., Mustafi, A. (2016). A Multi Criteria Document Clustering Approach Using Genetic Algorithm. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 1. Advances in Intelligent Systems and Computing, vol 410. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2734-2_25
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DOI: https://doi.org/10.1007/978-81-322-2734-2_25
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