Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, o… Mehr…
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview.The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.; PDF; Computing > Computer programming / software development > Algorithms & data structures, Springer Berlin Heidelberg<
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No. 9783540283492. Versandkosten:Instock, Despatched same working day before 3pm, zzgl. Versandkosten. Details...
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Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, o… Mehr…
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview.The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.; PDF; Computing > Computer programming / software development > Algorithms & data structures, Springer Berlin Heidelberg<
No. 9783540283492. Versandkosten:Instock, Despatched same working day before 3pm, zzgl. Versandkosten.
1Da einige Plattformen keine Versandkonditionen übermitteln und diese vom Lieferland, dem Einkaufspreis, dem Gewicht und der Größe des Artikels, einer möglichen Mitgliedschaft der Plattform, einer direkten Lieferung durch die Plattform oder über einen Drittanbieter (Marketplace), etc. abhängig sein können, ist es möglich, dass die von eurobuch angegebenen Versandkosten nicht mit denen der anbietenden Plattform übereinstimmen.
Buch in der Datenbank seit 2007-02-13T04:29:06+01:00 (Berlin) Detailseite zuletzt geändert am 2023-08-18T13:19:55+02:00 (Berlin) ISBN/EAN: 9783540283492
ISBN - alternative Schreibweisen: 3-540-28349-8, 978-3-540-28349-2 Alternative Schreibweisen und verwandte Suchbegriffe: Autor des Buches: mar, kogan, jacob, nicholas Titel des Buches: data, recent advances
Daten vom Verlag:
Autor/in: Jacob Kogan; Charles Nicholas; Marc Teboulle Titel: Grouping Multidimensional Data - Recent Advances in Clustering Verlag: Springer; Springer Berlin 268 Seiten Erscheinungsjahr: 2006-02-08 Berlin; Heidelberg; DE Sprache: Englisch 96,29 € (DE) 99,00 € (AT) 118,00 CHF (CH) Available XII, 268 p.
EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Algorithmen und Datenstrukturen; Verstehen; Excel; LA; MATLAB; algorithms; classification; clustering algorithm; correlation; data analysis; data clustering; data mining; text clustering; text mining; C; Data Structures and Information Theory; Information Storage and Retrieval; Statistical Theory and Methods; Mathematical Applications in Computer Science; Statistics and Computing; Automated Pattern Recognition; Computer Science; Informationstheorie; Informationsrückgewinnung, Information Retrieval; Data Warehousing; Wahrscheinlichkeitsrechnung und Statistik; Theoretische Informatik; Mathematische und statistische Software; Mustererkennung; BB
The Star Clustering Algorithm for Information Organization.- A Survey of Clustering Data Mining Techniques.- Similarity-Based Text Clustering: A Comparative Study.- Clustering Very Large Data Sets with Principal Direction Divisive Partitioning.- Clustering with Entropy-Like k-Means Algorithms.- Sampling Methods for Building Initial Partitions.- TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections.- Criterion Functions for Clustering on High-Dimensional Data. Includes supplementary material: sn.pub/extras;
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