Clustering Algorithms for Biological Data

Damodar Reddy Edla

ISBN 10: 3659454745 / ISBN 13: 9783659454745

Verlag: LAP LAMBERT Academic Publishing Sep 2013, 2013

Beschreibung

This item is printed on demand - Print on Demand Titel. Neuware -With the recent advances of data generation and acquisition systems and the success of several projects such as Human Genome, a large number of databases, especially in biological field are now available worldwide. The growing rate of such databases is also exponential. There is a need to explore and analyze such massive data to infer some inherent information. Clustering has been recognized as one of the widely used data mining techniques which is essential for data analysis to reveal natural structures and to identify interesting patterns in the underlying data. In the last decade, significant amount of research work has been carried out on cluster analysis and a large number of algorithms have been developed, particularly for biological data. Recently, much attention has been paid to develop various clustering algorithms based on neighborhood graphs such as minimum spanning tree (MST), Voronoi diagram and kd-trees. In this book, we mainly report on the hierarchical, partitional, density-based and graph-based clustering algorithms which are developed using such neighborhood graphs.Books on Demand GmbH, Überseering 33, 22297 Hamburg 148 pp. Englisch.

Bestandsnummer des Verkäufers 9783659454745

Bibliografische Details

• Titel: Clustering Algorithms for Biological Data
• Verlag: LAP LAMBERT Academic Publishing Sep 2013
• Erscheinungsjahr: 2013
• Sprache: Englisch
• ISBN-10: 3659454745
• ISBN-13: 9783659454745
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

With the recent advances of data generation and acquisition systems and the success of several projects such as Human Genome, a large number of databases, especially in biological field are now available worldwide. The growing rate of such databases is also exponential. There is a need to explore and analyze such massive data to infer some inherent information. Clustering has been recognized as one of the widely used data mining techniques which is essential for data analysis to reveal natural structures and to identify interesting patterns in the underlying data. In the last decade, significant amount of research work has been carried out on cluster analysis and a large number of algorithms have been developed, particularly for biological data. Recently, much attention has been paid to develop various clustering algorithms based on neighborhood graphs such as minimum spanning tree (MST), Voronoi diagram and kd-trees. In this book, we mainly report on the hierarchical, partitional, density-based and graph-based clustering algorithms which are developed using such neighborhood graphs.