A Link-Based Cluster Ensemble Approach for Improved Gene Expression Data Analysis

It is difficult from possibilities to select a most suitable effective way of clustering algorithm and its dataset, for a defined set of gene expression data, because we have a huge number of ways and huge number of gene expressions. At present many researchers prefer to use hierarchical clustering in different forms, this is no more totally optimal. Cluster ensemble research can solve this type of problem by automatically merging multiple data partitions from a wide range of different clusterings of any dimensions to improve both the quality and robustness of the clustering result. But we have many existing ensemble approaches using an association matrix to condense sample-cluster and co-occurrence statistics, and relations within the ensemble are encapsulated only at raw level, while the existing among clusters are totally discriminated. Finding these missing associations can greatly expand the capability of those ensemble methodologies for microarray data clustering. We propose general K-means cluster ensemble approach for the clustering of general categorical data into required number of partitions.