Evolutionary Algorithms for Robust Density-Based Data Clustering | Zendy

Amit Banerjee | Zendy

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Evolutionary Algorithms for Robust Density-Based Data Clustering

Author(s) -

Amit Banerjee

Publication year - 2013

Publication title -

isrn computational mathematics

Language(s) - English

Resource type - Journals

ISSN - 2090-7842

DOI - 10.1155/2013/931019

Subject(s) - cluster analysis , cure data clustering algorithm , computer science , correlation clustering , data mining , data stream clustering , outlier , canopy clustering algorithm , fuzzy clustering , algorithm , artificial intelligence , pattern recognition (psychology)

Density-based clustering methods are known to be robust against outliers in data; however, they are sensitive to user-specified parameters, the selection of which is not trivial. Moreover, relational data clustering is an area that has received considerably less attention than object data clustering. In this paper, two approaches to robust density-based clustering for relational data using evolutionary computation are investigated.

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