Title :
CCM: A Text Classification Model by Clustering
Author :
Nizamani, Sarwat ; Memon, Nasrullah ; Wiil, Uffe Kock ; Karampelas, Panagiotis
Author_Institution :
Maersk Mc-Kinney Moller Inst., Univ. of Southern Denmark, Odense, Denmark
Abstract :
In this paper, a new Cluster based Classification Model (CCM) for suspicious email detection and other text classification tasks, is presented. Comparative experiments of the proposed model against traditional classification models and the boosting algorithm are also discussed. Experimental results show that the CCM outperforms traditional classification models as well as the boosting algorithm for the task of suspicious email detection on terrorism domain email dataset and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. The overall finding is that applying a cluster based approach to text classification tasks simplifies the model and at the same time increases the accuracy.
Keywords :
electronic mail; information resources; pattern classification; pattern clustering; terrorism; text analysis; CCM; Newsgroups datasets; Reuters-21578; boosting algorithm; cluster based classification model; email detection; terrorism domain email dataset; text classification model; topic categorization; Accuracy; Boosting; Classification algorithms; Clustering algorithms; Electronic mail; Text categorization; Training; Boosting; Classification; Clustering; ID3; K-means; NB; SVM;
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-61284-758-0
Electronic_ISBN :
978-0-7695-4375-8
DOI :
10.1109/ASONAM.2011.76