A Possibilistic Density Based Clustering for Discovering Clusters of Arbitrary Shapes and Densities in High Dimensional Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Noha A. Yousri , Mohamed S. Kamel , Mohamed A. Ismail

ABSTRACT

Apart from the interesting problem of finding arbitrary shaped clusters of different densities, some applications further introduce the challenge of finding overlapping clusters in the presence of outliers. Fuzzy and possibilistic clustering approaches have therefore been developed to handle such problem, where possibilistic clustering is able to handle the presence of outliers compared to its fuzzy counterpart. However, current known fuzzy and possibilistic algorithms are still inefficient to use for finding the natural cluster structure. In this work, a novel possibilistic density based clustering approach is introduced, to identify the degrees of typicality of patterns to clusters of arbitrary shapes and densities. Experimental results illustrate the efficiency of the proposed approach compared to related algorithms. More... »

PAGES

577-584

Book

TITLE

Neural Information Processing

ISBN

978-3-642-34486-2
978-3-642-34487-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-34487-9_70

DOI

http://dx.doi.org/10.1007/978-3-642-34487-9_70

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038160393


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