Abstract
Multivariate dataset is clustered and compared for improvement for various clustering techniques using four well-known algorithms used in clustering techniques such as like k-Means, Fuzzy K-Means, Rough K-Means, and Fanny taking help of statistical programming environment R. Well-known dataset IRIS is used with 150 instances and 3 classes. Simulated finding speaks in favor of Rough K-Means LW algorithm with Funny as it can allocate all data with zero standard deviation. Dataset are also plotted for all the algorithms used here separately. Result can be extended for analysis of clustered data for any arbitrary dataset.
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Bhattacharjee, A.K., Dey, M., Dutta, D., Sett, S., Mukherjee, S., Deyasi, A. (2019). Comparative Study and Improvement of Various Clustering Techniques in Statistical Programming Environment. In: Mandal, J., Sinha, D., Bandopadhyay, J. (eds) Contemporary Advances in Innovative and Applicable Information Technology. Advances in Intelligent Systems and Computing, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-13-1540-4_15
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DOI: https://doi.org/10.1007/978-981-13-1540-4_15
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