9783846524343 - Discovering Clusters of Arbitrary Shapes and Densities in Data Streams: A density-based and grid-based approach to discover clusters in data streams de Magdy, Amr; ... (9 resultados)

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Taschenbuch. Condición: Neu. Discovering Clusters of Arbitrary Shapes and Densities in Data Streams | A density-based and grid-based approach to discover clusters in data streams | Amr Magdy (u. a.) | Taschenbuch | 116 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846524343 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The huge size of a continuously flowing data has put forward a number of challenges in data stream analysis. Exploration of the structure of streamed data represented a major challenge that resulted in introducing various clustering algorithms. However, current clustering algorithms still lack the ability to efficiently discover clusters of arbitrary densities in data streams. In this thesis, a new grid-based and density-based algorithm is proposed for clustering data streams. It addresses drawbacks of recent algorithms in discovering clusters of arbitrary densities. The algorithm uses an online component to map the input data to grid cells. An offline component is then used to cluster the grid cells based on density information. Relative density relatedness measures and a dynamic range neighborhood are proposed to differentiate clusters of arbitrary densities. The experimental evaluation shows considerable improvements upon the state-of-the-art algorithms in both clustering quality and scalability with different stream sizes and with higher dimensions. In addition, the output quality of the proposed algorithm is less sensitive to parameter selection errors. 116 pp. Englisch.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Magdy AmrAmr Magdy has finished his M.Sc. degree in Computer and System Engineering in Alexandria University, Egypt under supervision of Prof. Dr. Nagwa M. El-Makky and Assistant Prof. Noha A. Yousri. Their research interests include.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The huge size of a continuously flowing data has put forward a number of challenges in data stream analysis. Exploration of the structure of streamed data represented a major challenge that resulted in introducing various clustering algorithms. However, current clustering algorithms still lack the ability to efficiently discover clusters of arbitrary densities in data streams. In this thesis, a new grid-based and density-based algorithm is proposed for clustering data streams. It addresses drawbacks of recent algorithms in discovering clusters of arbitrary densities. The algorithm uses an online component to map the input data to grid cells. An offline component is then used to cluster the grid cells based on density information. Relative density relatedness measures and a dynamic range neighborhood are proposed to differentiate clusters of arbitrary densities. The experimental evaluation shows considerable improvements upon the state-of-the-art algorithms in both clustering quality and scalability with different stream sizes and with higher dimensions. In addition, the output quality of the proposed algorithm is less sensitive to parameter selection errors.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The huge size of a continuously flowing data has put forward a number of challenges in data stream analysis. Exploration of the structure of streamed data represented a major challenge that resulted in introducing various clustering algorithms. However, current clustering algorithms still lack the ability to efficiently discover clusters of arbitrary densities in data streams. In this thesis, a new grid-based and density-based algorithm is proposed for clustering data streams. It addresses drawbacks of recent algorithms in discovering clusters of arbitrary densities. The algorithm uses an online component to map the input data to grid cells. An offline component is then used to cluster the grid cells based on density information. Relative density relatedness measures and a dynamic range neighborhood are proposed to differentiate clusters of arbitrary densities. The experimental evaluation shows considerable improvements upon the state-of-the-art algorithms in both clustering quality and scalability with different stream sizes and with higher dimensions. In addition, the output quality of the proposed algorithm is less sensitive to parameter selection errors.