{"product_id":"enhancing-variants-of-k-means-von-raghavendra-chilamakur-rajendra-prasad-kypa-reuben-bernard-francis","title":"Enhancing Variants of K-Means","description":"\u003cp\u003eClustering analysis is one of the most commonly used data processing algorithms. Over half a century, K-means remains the most popular clustering algorithm because of its simplicity. Traditional K-means clustering tries to assign n data objects to k clusters starting with random initial centers. However, most of the k- means variants tend to compute distance of each data point to each cluster centroid for every iteration. We propose a fast heuristic to overcome this bottleneck with only marginal increase in Mean Squared Error (MSE). We observe that across all iterations of K-means, a data point changes its membership only among a small subset of clusters. Our heuristic predicts such clusters for each data point by looking at nearby clusters after the first iteration of k-means. We augment well-known variants of k- means like Enhanced K-means and K-means with Triangle Inequality using our heuristic to demonstrate its effectiveness. For various datasets, our heuristic achieves speed-up of up-to 3 times when compared to efficient variants of k-means.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9786139983803\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9786139983803","offer_id":39448080613469,"sku":"9786139983803","price":39.9,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/d2543271-f906-464a-8b4e-dd26b9659652.jpg?v=1773475168","url":"https:\/\/shop.autorenwelt.de\/products\/enhancing-variants-of-k-means-von-raghavendra-chilamakur-rajendra-prasad-kypa-reuben-bernard-francis","provider":"Autorenwelt Shop","version":"1.0","type":"link"}