A Prototype-Based Modified DBSCAN for Gene Clustering

In this paper, we propose, a novel DBSCAN method to cluster the gene expression data. The main problem of DBSCAN is its quadratic computational complexity. We resolve this drawback by using the prototypes produced from a squared error clustering method such as K-means. Then, the DBSCAN technique is...

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Bibliographic Details
Published in:Procedia technology 2012, Vol.6, p.485-492
Main Authors: Edla, Damodar Reddy, Jana, Prasanta K.
Format: Article
Language:English
Subjects:
Clustering
DBSCAN
Gene expression data
Prototype
Squared error method
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Summary:In this paper, we propose, a novel DBSCAN method to cluster the gene expression data. The main problem of DBSCAN is its quadratic computational complexity. We resolve this drawback by using the prototypes produced from a squared error clustering method such as K-means. Then, the DBSCAN technique is applied efficiently using these prototypes. In our algorithm, during the iterations of DBSCAN, if a point from an uncovered prototype is assigned to a cluster, then all the other points of such prototype belongs to the same cluster. We have carried out excessive experiments on various two dimensional artificial and multi-dimensional biological data. The proposed technique is compared with few existing techniques. It is observed that proposed algorithm outperforms the existing methods.
ISSN:2212-0173
2212-0173
DOI:10.1016/j.protcy.2012.10.058