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• W2613364025 abstract "In this paper, we discuss proportional data clustering. It emerges In many applications such as document clustering and Image classification using bag of visual words approach. When deploying mixture models for clustering, there Is always a problem of initialization, and It Is common to initialize using K-means algorithm. In proposed work, we present K-means clustering approach using different distance metrics. In particular, we propose the consideration of the Altchlson distance. Experimental results are presented using silhouette plots for showing divergence from the center, and confusion matrix Is used to validate our clustering of synthetic and real data sets of Images and texts. The algorithm with Altchlson distance metric results Into lower error rates." @default.
• W2613364025 created "2017-05-19" @default.
• W2613364025 creator A5085791174 @default.
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• W2613364025 date "2017-03-01" @default.
• W2613364025 modified "2023-09-27" @default.
• W2613364025 title "Proportional data clustering using K-means algorithm: A comparison of different distances" @default.
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• W2613364025 doi "https://doi.org/10.1109/icit.2017.7915506" @default.
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