Call For Paper Volume:4 Issue:10 Oct'2017 |

Mining Gene Expression Data Based Affinity Search Clustering Technique

Publication Date : 16/04/2015



Author(s) :

U.Prakash , D.Shanmugapriya , Anguvigneshguru.


Volume/Issue :
Volume 2
,
Issue 4
(04 - 2015)



Abstract :

An appreciative towards genetics and epi-genetic is necessary to deal with up by means of the pattern shift which is ongoing research in biomedical applications. Because of the development of new technologies, it has become very easier to collect the information of data regarding gene expression data in molecular ecology field. But analysis and examination of such type of data becomes very difficult since it consist of several genes and micro array data set samples. So the clustering the gene expression becomes a valuable solution in the various fields of application such as business, medical knowledge, and economics. Conversely, traditional clustering algorithms methods for gene expression data produces less results ,since it is particularly designed for specific categories of gene samples .In order to conquer these problems in this work presents a affinity search based clustering methods for clustering gene expression data set samples . Proposed work gene expression data set samples are grouped based on measuring similarity value with predefined affinity threshold value. This clustering method the sub clusters are grouped based on the k-Medoids algorithm. This AFC consider each gene as objects and gene expression data set samples as features. At the same time effectiveness is achieved higher than the traditional clustering method for gene expression data set samples


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Mining Gene Expression Data Based Affinity Search Clustering Technique

April 15, 2015