detecting outliers univariate outlier de singh vijendra (11 résultats)

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Singh VijendraVijendra Singh received the M.Tech degree in Computer Science and Engineering from Birla Institute of Technology, Mesra, India. His research interests include Data Mining, Pattern Recognition,and Soft Computation.He has.

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This report presents an integrated outlier detection method, which is named 'An Approach to Detect Outlier by Integrating Univariate Outlier Detection and K-means Algorithm'. It provides efficient outlier detection and data clustering capabilities in the presence of outliers, and based on filtering of the data after univariate analysis. This algorithm is divided into two stages. The first stage provides Univariate outlier analysis. The main objective of the second stage is an iterative removal of objects, which are far away from their cluster centroids by applying K-means algorithm. The removal occurs according to the minimisation of the value of sum of the distances of all the points to their respective centroid in all the clusters. Finally, we provide experimental results from the application of our algorithm on several datasets to show its effectiveness and usefulness. The empirical results indicate that the proposed method was successful in detecting outliers and promising in practice. 64 pp. Englisch.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This report presents an integrated outlier detection method, which is named 'An Approach to Detect Outlier by Integrating Univariate Outlier Detection and K-means Algorithm'. It provides efficient outlier detection and data clustering capabilities in the presence of outliers, and based on filtering of the data after univariate analysis. This algorithm is divided into two stages. The first stage provides Univariate outlier analysis. The main objective of the second stage is an iterative removal of objects, which are far away from their cluster centroids by applying K-means algorithm. The removal occurs according to the minimisation of the value of sum of the distances of all the points to their respective centroid in all the clusters. Finally, we provide experimental results from the application of our algorithm on several datasets to show its effectiveness and usefulness. The empirical results indicate that the proposed method was successful in detecting outliers and promising in practice.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This report presents an integrated outlier detection method, which is named ¿An Approach to Detect Outlier by Integrating Univariate Outlier Detection and K-means Algorithm¿. It provides efficient outlier detection and data clustering capabilities in the presence of outliers, and based on filtering of the data after univariate analysis. This algorithm is divided into two stages. The first stage provides Univariate outlier analysis. The main objective of the second stage is an iterative removal of objects, which are far away from their cluster centroids by applying K-means algorithm. The removal occurs according to the minimisation of the value of sum of the distances of all the points to their respective centroid in all the clusters. Finally, we provide experimental results from the application of our algorithm on several datasets to show its effectiveness and usefulness. The empirical results indicate that the proposed method was successful in detecting outliers and promising in practice.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch.