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- W155115054 abstract "This paper suggests over-segmenting the feature space for improved initialisation of the k-means clustering method. Two over-segmentations (OS) of a dataset are achieved and then fused to determine the best- possible initial means to perform the iterative process of k- means. The method is tested over both, image as well as non-image data. The image data is segmented using tex- ture features based on Gabor energy features (GEF). The method is compared with single-run k-means and k-means with multiple restarts. Encouraging results have been obtained while segmenting a variety of texture images and the non-image data. The 40% OS is found to give the best results. This technique addresses the issue of data cluster- ing in all the possible areas including data mining, pattern recognition, machine learning and statistics." @default.
- W155115054 created "2016-06-24" @default.
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- W155115054 date "2007-10-24" @default.
- W155115054 modified "2023-09-27" @default.
- W155115054 title "Over-Segmentation of Feature Space for Initialisation of K-means Clustering" @default.
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