Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ASONAM.2011.76 | Advances in Social Networks Analysis and Mining |
Keywords | Field | DocType |
classification model,suspicious email detection,comparative experiment,terrorism domain email dataset,traditional classification model,text classification model,new cluster,text classification task,newsgroups datasets,id3,ccm,clustering algorithms,clustering,accuracy,boosting,classification algorithms,boosting algorithm,classification,svm,terrorism,text analysis,k means | Categorization,k-means clustering,Data mining,Text mining,Computer science,Support vector machine,Boosting (machine learning),Artificial intelligence,ID3,Cluster analysis,Statistical classification,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-4375-8 | 2 | 0.37 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarwat Nizamani | 1 | 15 | 4.22 |
| Nasrullah Memon | 2 | 504 | 56.67 |
| Uffe Kock Wiil | 3 | 865 | 94.54 |
| Panagiotis Karampelas | 4 | 34 | 15.16 |