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Clustering has been used in literature to enhance classification accuracy. But most partitional clustering methods need the number of clusters as input and also they are sensitive to initialization. Although hierarchical clustering methods may be more effective in finding clustering structure of the dataset than partitional methods but hierarchical(More)
Supervised learning algorithms are trained with labeled data only. But labeling the data can be costly and hence the amount of labeled data available may be limited. Training the classifiers with limited amount of labeled data can lead to low classification accuracy. Hence pre-processing the data is required for getting better classification accuracy. Full(More)
Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have been solved by using DE based clustering methods but these methods may fail to find clusters hidden in subspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed in literature to find subspace clusters that are(More)
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