Heterogeneous Data Co-Clustering by Pseudo-Semantic Affinity Functions
نویسندگان
چکیده
The convergence between Web technology and multimedia production is enabling the distribution of content through dynamic media platforms such as RSS feeds and hybrid digital television. Heterogeneous data clustering is needed to analyse, manage and access desired information from this variety of information sources. This paper defines a new class of pseudo-semantic affinity functions that allow for a compact representation of cross-modal documents relations.
منابع مشابه
Identifying protein complexes from heterogeneous biological data.
With the increasing availability of diverse biological information for proteins, integration of heterogeneous data becomes more useful for many problems in proteomics, such as annotating protein functions, predicting novel protein-protein interactions and so on. In this paper, we present an integrative approach called InteHC (Integrative Hierarchical Clustering) to identify protein complexes fr...
متن کاملEvolutionary Image Co-clustering with User Feedbacks
Traditional image clustering systems are primarily based on visual and/or textual features. Such algorithms commonly suffer from the problem of semantic gap. General approach to overcome this problem is to incorporate user feedback. However, data extracted from certain domains like social networks, web-blogs etc. is evolving in nature. In other words, data collected from such domains over small...
متن کاملPseudo-centroid clustering
Pseudo-Centroid Clustering replaces the traditional concept of a centroid expressed as a center of gravity with the notion of a pseudo-centroid (or a coordinate free centroid) which has the advantage of applying to clustering problems where points do not have numerical coordinates (or categorical coordinates that are translated into numerical form). Such problems, for which classical centroids ...
متن کاملSemantic Correspondence of Database Schema from Heterogeneous Databases using Self-Organizing Map
This paper provides a framework for semantic correspondence of heterogeneous databases using selforganizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to...
متن کاملCo-clustering for Weblogs in Semantic Space
Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able to capture the underlying user navigational interest and content preference simultaneously. In this paper we will present a novel web co-clust...
متن کامل