There is a need to organize a large set of documents into categories through clustering so as to facilitate searching and finding the relevant information on the web with large number of documents becomes easier and quicker. Hence we need more efficient clustering algorithms for organizing documents. Clustering on large text dataset can be effectively done using partitional clustering algorithm...

In the present time, evaluating the performance of banks is one of the important subjects for societies and the bank managers who want to expand the scope of their operation. One of the non-parametric approaches for evaluating efficiency is data envelopment analysis(DEA). By a mathematical programming model, DEA provides an estimation of efficiency surfaces. A major problem faced by DEA is that...

In this paper, we propose a hybrid approach for microarray image analysis, which is to quantify the intensity of each spot and locate differentially articulated genes with the aid of image processing and machine learning techniques. Initially we employ a hill-climbing automatic gridding and spot quantification technique, which takes a microarray image (or a sub-grid) as input, and makes no assu...

Clustering of proteins is important in the field of bioinformatics. Clustering of proteins is used for analyzing the proteins to determine their functions and structure. The number of partitioning techniques, hierarchical methods and graph-based methods are available for clustering protein sequences. In this paper, we propose a hybrid fuzzy technique for clustering proteins based on its seconda...

Data clustering is a process of arranging similar data into groups. Data clustering is a common technique for data analysis and is used in many fields, including data mining, pattern recognition and image analysis. In this paper a hybrid clustering algorithm based on K-mean is described. K-means clustering is a common and simple approach for data clustering but this method has some limitation s...

Emphasis is an important aspect of speech that conveys the focus of utterances, and modeling of this emphasis has been an active research field. Previous work has modeled emphasis using state clustering with an emphasis contextual factor indicating whether or not a word is emphasized. In addition, cluster adaptive training (CAT) makes it possible to directly optimize model parameters for cluste...

Many methods have been developed for reverse engineering gene networks from time series expression data. However, when the number of genes and the complexity of regulation increase, it becomes increasingly difficult to infer gene networks. To tackle this scalability problem, this study presents an approach with two phases: gene clustering and network reconstruction. To perform gene clustering, ...

This paper presents a method using hybridization of self organizing map (SOM ), particle swarm optimization(PSO) and k-means clustering algorithm for document clustering. Document representation is an important step for clustering purposes. The common way of represent a text is bag of words approach. This approach is simple but has two drawbacks viz. synonymy and polysemy which arise because of...

We present a hybrid clustering algorithm of multiple information sources via tensor decomposition, which can be regarded an extension of the spectral clustering based on modularity maximization. This hybrid clustering can be solved by the truncated higher-order singular value decomposition (HOSVD). Experimental results conducted on the synthetic data have demonstrated the effectiveness. keyword...