9786139983803 - enhancing variants of k-means von chilamakur, raghavendra; kypa, rajendra prasad; francis, reuben bernard (10 Ergebnisse)
Enhancing Variants of K-Means
Chilamakur, Raghavendra; Kypa, Rajendra Prasad; Francis, Reuben Bernard
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Enhancing Variants of K-Means
Chilamakur, Raghavendra/ Kypa, Rajendra Prasad/ Francis, Reuben Bernard
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Taschenbuch. Zustand: Neu. Enhancing Variants of K-Means | Raghavendra Chilamakur (u. a.) | Taschenbuch | 64 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139983803 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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 objec…ts 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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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - 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 da…ta 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. 64 pp. Englisch.
Enhancing Variants of K-Means
Chilamakur, Raghavendra; Kypa, Rajendra Prasad; Francis, Reuben Bernard
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Enhancing Variants of K-Means
Chilamakur, Raghavendra; Kypa, Rajendra Prasad; Francis, Reuben Bernard
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chilamakur RaghavendraC. Raghavendra, pursuing Ph.D in Computer Science & Engineering from Bharath University, Chennai. Presently, he is working as Asst. Professor, CSE Dept., Institute of Aeronautical En…gineering, Hyderabad. His res.
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Taschenbuch. Zustand: Neu. 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 o…bjects 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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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 ob…jects 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.
