As the number of RSS news feeds continue to increase over the Internet, it becomes necessary to minimize the workload of the user who is otherwise required to scan through huge number of news articles to find related articles of interest, which is a tedious and often an impossible task. In order to solve this problem, we present a novel approach, called InFRSS, which consists of a correlation-based phrase matching (CPM) model and a fuzzy compatibility clustering (FCC) model. CPM can detect RSS news articles containing phrases that are the same as well as semantically alike, and dictate the degrees of similarity of any two articles. FCC identifies and clusters non-redundant, closely related RSS news articles based on their degrees of similarity and a fuzzy compatibility relation. Experimental results show that (i) our CPM model on matching bigrams and trigrams in RSS news articles outperforms other phrase/keyword-matching approaches and (ii) our FCC model generates high quality clusters and outperforms other well-known clustering techniques.
Maria Soledad REF clustered the RSS news articles, gave available similarity metrics of RSS articles.
Utilizing Phrase-Similarity Measures for Detecting and Clustering Informative RSS News Articles
Integr. Comput. Aided Eng.
Integr. Comput. Aided Eng.
Computer Science