{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T21:04:03Z","timestamp":1705093443117},"reference-count":23,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Info. Know. Mgmt."],"published-print":{"date-parts":[[2014,9]]},"abstract":"<jats:p> In literature studies, high-dimensional data reduces the efficiency of clustering algorithms and maximises execution time. Therefore, in this paper, we propose an approach called a BV-kmeans (Bayesian Vectorisation along with k-means) that aims to improve document representation models for text clustering. This approach consists of integrating the k-means document clustering with the Bayesian Vectoriser that is used to compute the probability distribution of the documents in the vector space in order to overcome the problems of high-dimensional data and lower the consumption time. We have used various similarity measures which are namely: K divergence, Squared Euclidean distance and Squared \u03c7<jats:sup>2<\/jats:sup> distance in order to determine the effective metrics for modelling the similarity between documents with the proposed approach. We have evaluated the proposed approach on a set of common newspaper websites that have highly dimensional data. Experimental results show that the proposed approach can increase the degree to which a cluster encases documents from a specific category by 85%. This is in comparison with the standard k-means algorithm and it has succeeded in lowering the runtime using the proposed approach by 95% compared to the standard k-means algorithm. <\/jats:p>","DOI":"10.1142\/s0219649214500269","type":"journal-article","created":{"date-parts":[[2014,9,19]],"date-time":"2014-09-19T07:46:30Z","timestamp":1411112790000},"page":"1450026","source":"Crossref","is-referenced-by-count":6,"title":["Improved Text Clustering Using k-Mean Bayesian Vectoriser"],"prefix":"10.1142","volume":"13","author":[{"given":"Hanan M.","family":"Alghamdi","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Umm Al-Qura University, Al-Gunfdh, Saudi Arabia"},{"name":"Faculty of Computing, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Selamat","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nor Shahriza Abdul","family":"Karim","sequence":"additional","affiliation":[{"name":"Computer &amp; Information Science Department, Prince Sultan University, 66833 Rafha Street, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2014,10,10]]},"reference":[{"key":"rf1","first-page":"4033","volume":"6","author":"Al-diabat M.","year":"2012","journal-title":"Applied Mathematical Sciences"},{"key":"rf5","first-page":"124","volume":"2","author":"Alsaleem S.","year":"2011","journal-title":"International Arab Journal of e-Technology"},{"key":"rf6","first-page":"41","volume":"12","author":"Alsulami B. 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