Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/11831
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dc.contributor.authorTripathi, S.
dc.contributor.authorSharma, A.K.
dc.contributor.authorMishra, R., B.
dc.contributor.authorPandey, B.
dc.date.accessioned2020-03-31T08:35:41Z-
dc.date.available2020-03-31T08:35:41Z-
dc.date.issued2016
dc.identifier.citationInternational Journal of Control Theory and Applications, 2016, Vol.9, Specialissue11, pp.5529-5540en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11831-
dc.description.abstractIn this paper K means Clustering Algorithm is used for clustering of candidate genes related to human episodic memory. The clustering of genes is based on gene-gene interaction score. The clusters are supposed to be formed so that distribution of cluster as well as overall interaction Score of clusters should be better. The K-means clustering technique applied to cluster the genes such as in tool STRING 9.1 provides cluster outcome. We compare the results of K means Clustering provided by STRING 9.1 with our K means Clustering Algorithm. The results obtained using K-means shows that clusters formed have better distribution of genes. International Science Press.en_US
dc.titleK means clustering for gene-gene interaction in episodic memoryen_US
dc.typeArticleen_US
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