Data Mining: Foundations and Intelligent Paradigms : Volume 1: Clustering, Association and Classification

Lakhmi C Jain

ISBN 10: 3642430937 / ISBN 13: 9783642430930

Verlag: Springer Berlin Heidelberg, 2014

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled 'DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification' we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Bestandsnummer des Verkäufers 9783642430930

Bibliografische Details

• Titel: Data Mining: Foundations and Intelligent Paradigms : Volume 1: Clustering, Association and Classification
• Verlag: Springer Berlin Heidelberg
• Erscheinungsjahr: 2014
• Sprache: Englisch
• ISBN-10: 3642430937
• ISBN-13: 9783642430930
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 155 H x 20 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 20 mm
• Gewicht: 534 Gramm

Über diesen Titel

Inhaltsangabe

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Von der hinteren Coverseite

Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 1of this three volume series, we have brought together contributions from some of the most prestigious researchers in the fundamental data mining tasks of clustering, association and classification. Each of the chapters is self contained. Theoreticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in these aspects of data mining.