Clustering is the most prominent data mining technique used for grouping the data into clusters based on distance measures. With the advent growth of high dimensional data such as microarray gene expression data, and grouping high dimensional data into clusters will encounter the similarity between the objects in the full dimensional space is often invalid because it contains different types of...

Advances made to the traditional clustering algorithms solves the various problems such as curse of dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we apply it to high dimensional data it causes the dimensional disaster problem due to high...

Clustering of variables is the task grouping similar into different groups. It may be useful in several situations such as dimensionality reduction, feature selection, and detect redundancies. In present study, we combine two methods features clustering around latent (CLV) algorithm k-means based co-clustering (kCC). Indeed, classical CLV cannot applied to high dimensional data because this app...