Comparison and analysis of tag-ranking algorithms based on clustering

Abstract

Most information in Web 2.0 is made by users and classified by tags assigned by users. Tag-related services and research are focused on work such as automatic tagging and tag-cloud composition; however, classifying media resources and information according to tags and providing the results to users is not still up to the mark. In this paper, image resources and their tag information scattered in the web are collected and a tag-pair weight matrix is created, according to the relations and semantic similarities between tags. To overcome the problems of the existing system, a tag-pair weight matrix-based tag clustering (TBTC) algorithm was proposed to find highly related tags. The threshold used for clustering in this algorithm was studied, and an optimal threshold with high cluster cohesion was determined. Finally, as an experiment, 500 images with the keyword 'tomato' were searched from the Flickr website and highly related tags were derived from the proposed algorithm. The results of this research were examined and compared with the results of existing studies. It was found that the proposed research showed more advanced accuracy and precision than earlier methods. © 2016 SERSC.