Enhancing Variants of K-Means - Softcover

Chilamakur, Raghavendra; Kypa, Rajendra Prasad; Francis, Reuben Bernard

Softcover

ISBN 10: 6139983800 ISBN 13: 9786139983803

Verlag: LAP LAMBERT Academic Publishing, 2019

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

C. Raghavendra, pursuing Ph.D in Computer Science & Engineering from Bharath University, Chennai. Presently, he is working as Asst. Professor, CSE Dept., Institute of Aeronautical Engineering, Hyderabad. His research interests are Image processing & Security, Big Data.

Details zur Ausgabe

• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsdatum: 2019
• Sprache: Englisch
• ISBN 10: 6139983800
• ISBN 13: 9786139983803
• Einband: Taschenbuch
• Anzahl der Seiten: 64
• Kontakt zum Hersteller: Nicht verfügbar
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