Spam Detection using Baysian with Pattren Discovery
Asmeeta Mali. DYPIET, University of Pune (Maharashtra), India.
Manuscript received on 21 July 2013 | Revised Manuscript received on 28 July 2013 | Manuscript published on 30 July 2013 | PP: 139-143 | Volume-2 Issue-3, July 2013 | Retrieval Number: C0742072313/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Text mining is nothing but the discovery of interesting knowledge in text documents. But there is a big challenging issue that how to guarantee the quality of discovered relevant features. And that are in the text documents for describing user preferences because of the large number of terms, patterns and noise. For text mining there are basically two types of approaches; one is term based approach and another is phrase based approach. But term based approach suffered with the problem of polysemy and synonymy. And phrase based approach suffered with low frequency occurrence. But phrase based approachs are better than the term based approachs. But pattern based approach is better than the term based and phrase based approach. The proposed method is an innovative and effective pattern discovery technique. This method includes two main processes pattern deploying and inner pattern evaluation. This paper presents an effective technique to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Using Baysian filtering algorithm and effective pattern Discovery technique we can detect the spam mails from the email dataset with good correctness of term.
Keywords: Text Mining, Information Filtering, Pattern Mining, Sequential Pattern, Closed Sequential Patterns.
Scope of the Article: Text Mining