Abstract

In this paper we propose an approach to extract community structures in the Web by considering a community structure as a group of content creators that manifests itself as a set of interlinked pages. We abstract the community structure as a dense bipartite graph (DBG) over a group of Web pages and proposed an algorithm to extract the DBGs from the given data set. Also, a high-level community is abstracted as a DBG over a set of a low-level communities. Using the proposed approach, a community hierarchy can be constructed for the given data set that generalizes a large number of low-level communities into a few highlevel communities. Using the proposed approach, we have extracted a a three-level community hierarchy from 10 GB TPEC (Text PEtrieval Conference) data set. We believe that the extracted community hierarchy facilitates easy analysis of the low-level communities, helps in reorganizing the Web sites, and provides a way to understand the sociology of the Web

Abstract

In E-commerce sites, recommendation systems are used torecommend products to their customers. Collaborative filtering (CF) iswidely employed approach to recommend products. In the literature,researchers are making efforts to improve the scalability and onlineperformance of CF. In this paper we propose a graph based approach toimprove the performance of CF. We abstract a neighborhood communityof a given customer through dense bipartite graph (DBG). Given adata set of customer preferences, a group of neighborhood customersfor a given customer is extracted by extracting corresponding DBG.The experimental results on the MovieLens data set show that therecommendation made with the proposed approach matches closelywith the recommendation of CF. The proposed approach possesses apotential to adopt to frequent changes in the product preference data set.

Abstract

Image databases index in them using features extracted from the image. The indexing scheme is decided apriori and is optimized for a specific querying criterion. This is not suitable for a generic database of images which may be queried by multiple users based on different criteria. IN this paper, we present a flexible scheme which adapts itself to the user 's preferences. Though the method uses a conservative set of features during indexing that includes a large number and type of fundamental features the query processing time does not increase due to this redundancy. A boot-strapping mechanism allows the user to build up a "desired class" from a few samples. The feature selection computation scales linearly with the size of the desired class, rather than that of the entire database. THis feature makes our algorithm viable for very large databases. We present an implementation of our scheme and some results from it.

Abstract

Fuzzy thresholding schemes preserve the structural details embedded in the original gray distribution. In this paper, various fuzzy thresholding schemes are analysed in detail. Thresholding scheme based on fuzzy clustering has been extended to a possibilistic framework. The characteristic di!erence for assignment of membership of fuzzy algorithms and their correspondence with conventional hard thresholding schemes have been investigated. A possible direction towards unifying a number of hard and fuzzy thresholding schemes has been presented. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved