Enhancing Variants of K-Means

Language: English

Published by LAP LAMBERT Academic Publishing Jan 2019, 2019

6139983800 / 9786139983803

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This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

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Title
Enhancing Variants of K-Means
Author
Raghavendra Chilamakur
Publisher
LAP LAMBERT Academic Publishing Jan 2019
Publication year
2019
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6139983800
ISBN 13
9786139983803
Item weight
113 grams
Dimensions
220x150x5 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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