Feature space high dimensionality is a well-known problem in text classification and web mining domains, it caused mainly by the large number of vocabularies contained within documents. Several methods were applied to select most useful important features over years; however, performance such still improvable from different aspects as computational cost accuracy. This research presents an enhan...

In recent years we have experienced an increase in the usage of tags to describe resources. However, the free nature of tagging presents some challenges regarding the search and exploration of tag spaces. In order to deal with these challenges we propose the Semantic Tag Clustering Search (STCS) framework. The framework first groups syntactic variations using several measures based on the Leven...

I investigate the complimentary roles of similarity on research interests, and of departmental co-membership on promoting new collaborative ties among professors at an elite American research university. Similarity is measured as cosine similarity of tf-idf calculations of their abstracts for published work, and is recalculated for each year in the study period. I find that similarity is a stro...

Ordered sets of documents are encountered more and more in information distribution systems, such as information retrieval systems (IRS). Classical similarity measures for ordinary sets of documents hence need to be extended to these ordered sets. This is done in this paper using fuzzy set techniques. First a general similarity measure is developed which contains the classical strong similarity...

Deep Web database clustering is a key operation in organizing Deep Web resources. Cosine similarity in Vector Space Model (VSM) is used as the similarity computation in traditional ways. However it cannot denote the semantic similarity between the contents of two databases. In this paper how to cluster Deep Web databases semantically is discussed. Firstly, a fuzzy semantic measure, which integr...