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Clustering, Fuzzy C-means Clustering, Random data set, Cluster center, Membership function, Time series prediction, Error analysis
Clustering is partitioning of data set into subsets (clusters), so that the data in each subset share some common trait. In this paper, an algorithm has been proposed based on Fuzzy C-means clustering technique for prediction of adsorption of cadmium by hematite. The original data elements have been used for clustering the random data set. The random data have been generated within the minimum and maximum value of test data. The proposed algorithm has been applied on random dataset considering the original data set as initial cluster center. A threshold value has been taken to make the boundary around the clustering center. Finally, after execution of algorithm, modified cluster centers have been computed based on each initial cluster center. The modified cluster centers have been treated as predicted data set. The algorithm has been tested in prediction of adsorption of cadmium by hematite. The error has been calculated between the original data and predicted data. It has been observed that the proposed algorithm has given better result than the previous applied methods.
Satyendra Nath Mandal, Suhit Sinha, Saptarisha Chatterjee, Sankha Subhra Mullick, Sriparna Das, "Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.12, pp.32-40, 2012. DOI:10.5815/ijisa.2012.12.05
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