چکیده مقاله
Clustering is the task of grouping related and similar data without any prior knowledge about the labels In some real world applications, we face huge amounts of unstructuredtextual data with no organization In these situations, clustering is a primitive operation that needs to be done to help future e commerce tasks Clustering can be used to enhancedifferent e commerce applications like recommender systems, customer relationshipmanagement systems or personal assistant agents In this paper we propose a new method for text clustering, by constructing a term correlation graph, and then extracting topic wordsets from it and finally, categorizing each document to its related topic with the help of a classification algorithm like SVM This method provides a natural and understandable description for clusters by their topic word sets, and it also enables us to decide the clusterof documents only when needed and in a parallel fashion, thus significantly reducing the offline processing time Our clustering method also outperforms the well known k means clustering algorithm according to clustering quality measures
کلیدواژهها
نویسندگان
Amir Mehdi Ghazifard
E Learning Department,University of Isfahan, Isfahan, Iran
Mohammadreza Shams
ECE Department,University of Tehran, Tehran, Iran
Zeinab Shamaee
ECE Department,Isfahan University of Technology, Isfahan, Iran
شیوه ارجاع
Ghazifard, Amir Mehdi and Shams, Mohammadreza and Shamaee, Zeinab,1392,Topic Word Set-Based Text Clustering,7th International Conference on e- Commerce whit focus on e-Security,Kish Island
ارائهشده در
مجموعه مقالات هفتمین کنفرانس بین المللی تجارت الکترونیک در کشورهای در حال توسعه با رویکرد بر امنیت ECDC201328 فروردین 1392 · جزیره کیش