Clustering High--Dimensional Data [electronic resource] : First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers / edited by Francesco Masulli, Alfredo Petrosino, Stefano Rovetta.
Contributor(s): Masulli, Francesco [editor.] | Petrosino, Alfredo [editor.] | Rovetta, Stefano [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 7627Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015Description: IX, 149 p. 41 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783662485774.Subject(s): Computer science | Algorithms | Database management | Data mining | Information storage and retrieval | Artificial intelligence | Computer Science | Database Management | Information Systems Applications (incl. Internet) | Artificial Intelligence (incl. Robotics) | Information Storage and Retrieval | Data Mining and Knowledge Discovery | Algorithm Analysis and Problem ComplexityAdditional physical formats: Printed edition:: No titleDDC classification: 005.74 Online resources: Click here to access online
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Springer eBooksSummary: This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the general subject and issues of high-dimensional data clustering; present examples of techniques used to find and investigate clusters in high dimensionality; and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering. .
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This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the general subject and issues of high-dimensional data clustering; present examples of techniques used to find and investigate clusters in high dimensionality; and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering. .
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