Abstract
The clustering is one of the fundamental steps in the field of data mining. This is widely used in various real-life applications for realizing potential information from the datasets. However, this particular procedure is intensive in term of computation. So, the clustering procedure is preferred with the optimal number of clusters. In this paper, the authors have suggested a novel objective function which is more effective in clustering with the optimal number of clusters detected through cluster validity indices. The proposed objective function based clustering approach is implemented on different types of images and the outcomes depict effective performance in terms of cluster quality based on segmentation entropy, and cluster partitioning time. The results are comparable with other related works and the satisfactory outcome is attained.
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Chowdhury, K., Chaudhuri, D., Pal, A.K. (2018). A Novel Objective Function Based Clustering with Optimal Number of Clusters. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Methodologies and Application Issues of Contemporary Computing Framework. Springer, Singapore. https://doi.org/10.1007/978-981-13-2345-4_3
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DOI: https://doi.org/10.1007/978-981-13-2345-4_3
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