Clustering is the task of grouping a set of objects in such a way that objects in the same group (called cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). The dimension can be reduced by using some techniques of dimension reduction. Recently new non linear methods introduced for reducing the dimensionality of such data called Locally Li...

A new method for unsupervised image category clustering is presented, based on a continuous version of a recently introduced information theoretic principle, the information bottleneck (IB). The clustering method is based on hierarchical grouping: Utilizing a Gaussian mixture model, each image in a given archive is first represented as a set of coherent regions in a selected feature space. Imag...

Mining user patterns of log files can provide significant and useful informative knowledge. This paper present an approach for mining similarity of interest among web users from their past access behaviors. Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent patter...

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Text-clustering is to divide a collection of textdocuments into different categories so that documents in the same category describe the same topic such as...

How to organize and present search results plays a critical role in the utility of search engines. Due to the unprecedented scale of the Web and diversity of search results, the common strategy of ranked lists has become increasingly inadequate, and clustering has been considered as a promising alternative. Clustering divides a long list of disparate search results into a few topic-coherent clu...

We present a method for real-time clustering of VPLs obtained from Reflective Shadow Maps (RSM). The clusters are treated as area light sources and used to approximate indirect illumination. The spatial extent of a cluster is used to deduce the shape and size of the respective area light source. Our method is fully GPU-based and avoids flickering by temporally coherent reinitialization of the c...

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets....

Transport and mixing processes in fluid flows are crucially influenced by coherent structures and the characterization of these Lagrangian objects is a topic of intense current research. While established mathematical approaches such as variational methods or transfer-operatorbased schemes require full knowledge of the flow field or at least high-resolution trajectory data, this information may...

In this work we explore the possible enhancement of the document clustering results, and in particular clustering of news articles from the web, when using word-based n-grams during the keyword extraction phase. We present and evaluate a weighting approach that combines clustering of news articles derived from the web using n-grams, extracted from the articles at an offline stage. We compared t...