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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.
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TextSeries: Lecture Notes in Computer SciencePublication details: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015Edition: 1st ed. 2015Description: IX, 149 p. 41 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783662485774Subject(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 ComplexityDDC classification: 005.74 LOC classification: QA76.9.D3Online 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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