Discovering Clusters of Arbitrary Shapes and Densities in Data Streams
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Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand 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.

Von Händler/Antiquariat, BuchWeltWeit Inh. Ludwig Meier e.K. [57449362], Bergisch Gladbach, Germany.
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.

Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, 11, zzgl. Versandkosten.
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.

Lieferung aus: Deutschland, Versandkostenfrei.
Discovering Clusters of Arbitrary Shapes and Densities in Data Streams: 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. Englisch, Taschenbuch.

Lieferung aus: Deutschland, Next Day, plus shipping.
Erscheinungsdatum: 11/2011, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Discovering Clusters of Arbitrary Shapes and Densities in Data Streams, Titelzusatz: A density-based and grid-based approach to discover clusters in data streams, Autor: Magdy, Amr // M. El-Makky, Nagwa // A. Yousri, Noha, Verlag: LAP Lambert Acad. Publ., Sprache: Englisch, Rubrik: Informatik, Seiten: 116, Informationen: Paperback, Gewicht: 189 gr, Verkäufer: averdo.

Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Erscheinungsdatum: 11/2011, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Discovering Clusters of Arbitrary Shapes and Densities in Data Streams, Titelzusatz: A density-based and grid-based approach to discover clusters in data streams, Autor: Magdy, Amr // M. El-Makky, Nagwa // A. Yousri, Noha, Verlag: LAP Lambert Acad. Publ., Sprache: Englisch, Rubrik: Informatik, Seiten: 116, Informationen: Paperback, Gewicht: 189 gr, Verkäufer: averdo.

Von Händler/Antiquariat, MARZIES.de Buch- und Medienhandel, 14621 Schönwalde-Glien.
Kartoniert / Broschiert, neu, 2017-10-25.