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Clustering 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.
| Autorius: | Raghavendra Chilamakur, Rajendra Prasad Kypa, Reuben Bernard Francis, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2019 |
| Knygos puslapių skaičius: | 64 |
| ISBN-10: | 6139983800 |
| ISBN-13: | 9786139983803 |
| Formatas: | 220 x 150 x 4 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Enhancing Variants of K-Means“
