Background. 16S rRNA gene sequences are routinely assigned to operational taxonomic units (OTUs) that are then used to analyze complex microbial communities. A number of methods have been employed to carry out the assignment of 16S rRNA gene sequences to OTUs leading to confusion over which method is optimal. A recent study suggested that a clustering method should be selected based on its abil...

As a useful data mining technique biclustering identifies local patterns from gene expression data. A bicluster of a gene expression dataset is a subset of genes which exhibit similar expression patterns along a subset of conditions. In this paper a new method is introduced based on greedy search algorithm combined with the evolutionary technique particle swarm optimization for the identificati...

We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets to estimate the subspaces. As the geometric structure of the clusters (linear subspaces) forbids proper performance of general distance based appro...

Recently, a number of methods have been proposed for semi-supervised clustering that employ supervision in the form of pairwise constraints. We describe a probabilistic model for semisupervised clustering based on Hidden Markov Random Fields (HMRFs) that incorporates relational supervision. The model leads to an EMstyle clustering algorithm, the E-step of which requires collective assignment of...

In this paper, a method for anonymization of social networks by clustering of k-edge-connected subgraphs (SACK) is presented. Previous anonymization algorithms do not consider distribution of nodes in social network graph according to their attributes. SACk tries to focus on this aspect that probability of existence of an edge between two nodes is related to their attributes and this leads to a...

Due to increase of the mining of compressed data from various types of applications. Clustering is the process for resolving that type of applications. Traditionally we are using different clustering applications for grouping elements with equal priority. And we represent single clustering process with multi dimensional data grouping using clustering but the only problem is performance of group...