Topic-based clustering of news articles

Recent years have witnessed an explosion in the availability of news articles on the World Wide Web. Although searchengines’ algorithms have made it easier to locate these documents, they still require considerable effort on the part of the user since most search engine algorithms look for keywords and do not take the contents of the entire article into context. We propose a system that clusters articles based on their topics. More specifically, we have focused on applying text mining methods to help solve the problems faced by a media organization or public relations department. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval – clustering, Information filtering, Selection process. General Terms Data mining, Text Mining, Clustering. Keywords

• Type: Conference
• Year: 2004
• Where: ACMSE
• Authors: Najaf Ali Shah, Ehab M. ElBahesh
• Keywords: ACMSE 2004, Search Engine Algorithms, Text Mining, Text Mining Methods, Theoretical Computer Science