Multi Density DBSCAN

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clu...

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Bibliographic Details
Published inIntelligent Data Engineering and Automated Learning - IDEA 2011 Vol. 6936; pp. 446 - 453
Main Authors Ashour, Wesam, Sunoallah, Saad
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2011
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Arbitrary Shape
Artificial intelligence
Clustering
DBSCAN
Information retrieval
variable Densities
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Summary:Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases.DBSCAN clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. DBSCAN cannot find clusters based on difference in densities. We extend the DBSCAN algorithm so that it can also detect clusters that differ in densities and without the need to input the value of Eps because our algorithm can find the appropriate value for each cluster individually by replacing Eps by Local cluster density.
ISBN:9783642238772
3642238777
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-23878-9_53