In this paper we present a probabilistic and continuous framework for supervised image category modelling and matching as well as unsupervised clustering of image space into image categories. A generalized GMM-KL framework is described in which each image or image-set (category) is represented as a Gaussian mixture distribution and images (categories) are compared and matched via a probabilisti...

The subject of wireless networks has recently gained a lot of attention from research community to investigate. The well-organized subdivision of a wireless network into coherent, mostly non-overlapping clusters of physically close nodes is a significant building block in the design of well-organized upper layer network functions such as energy saving, routing, stability, coverage and data aggr...

OSCAR is a web platform for cluster and crossspecies analysis of microarray data. It provides a comprehensive but friendly environment to both users and algorithm developers. For users, OSCAR provides cluster tools for both single and multiple species data, together with interactive analysis features. For single species data, OSCAR currently provides Hierarchical Clustering, K-means, partition ...

This paper presents a new method to identify classes during object-oriented software development. The approach uses process mapping and clustering technique for achieving unambiguous transition from requirements to the definition of object-oriented classes. We draw analogy between forming cells in cellular manufacturing and class formation in object-oriented software development and accordingly...

We propose a new procedure for clustering nucleotide sequences based on the “Laplacian Eigenmaps” and Gaussian Mixture modelling. This proposal is then applied to a set of 100 DNA sequences from the mitochondrially encoded NADH dehydrogenase 3 (ND3) gene of a collection of Platyhelminthes and Nematoda species. The resulting clusters are then shown to be consistent with the gene phylogenetic tre...

The automatic analysis of spatial data sets presumes to have techniques for interpretation and structure recognition. Such procedures are especially needed in GIS and digital cartography in order to automate the timeconsuming data update and to generate multi-scale representations of the data. In order to infer higher level information from a more detailed data set, coherent, homogeneous struct...

OSCAR is a web platform for cluster and cross-species analysis of microarray data. It provides a comprehensive but friendly environment to both users and algorithm developers. For users, OSCAR provides cluster tools for both single and multiple species data, together with interactive analysis features. For single species data, OSCAR currently provides Hierarchical Clustering, K-means, partition...

Discovering groups of genes that share common expression profiles is an important problem in DNA microarray analysis. Unfortunately, standard bi-clustering algorithms often fail to retrieve common expression groups because (1) genes only exhibit similar behaviors over a subset of conditions, and (2) genes may participate in more than one functional process and therefore belong to multiple group...

Context. Star-forming galaxies are highly biased tracers of the underlying dark matter density field. Their clustering can be studied through the cosmic infrared background (CIB) anisotropies. These anisotropies have been measured from 100 μm to 2 mm in the past few years. Aims. In this paper, we present a fully parametric model allowing joint analysis of these recent observations. Methods. To ...

In many data analysis tasks, one is often confronted with the problem of selecting features from very high dimensional data. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. To overcome this problem it is frequently assumed that either features independently influence the class variable or do so only involving pairwise feature...