Spam Filtering Approach based on Weighted Version of Possibilistic c-Means


  • Sarab M. Hameed Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq.
  • Marwan B. Mohammed Presidency of Al-Nahrain University, Al-Nahrain University, Baghdad, Iraq


Possibilistic c-Means (PCM) algorithm, Weighted PCM, Naïve Bayes, Spam filtering


A principal problem of any internet user is the increasing number of spam, which became a great problem today. Therefore, spam filtering has become a research fo-cus that attracts the attention of several security researchers and practitioners. Spam filtering can be viewed as a two-class classification problem. To this end, this paper proposes a spam filtering approach based on Possibilistic c-Means (PCM) algorithm and weighted distance coined as (WFCM) that can efficiently distinguish between spam and legitimate email messages. The objective of the formulated fuzzy problem is to construct two fuzzy clusters: spam and email clusters. The weight assignment is set by information gain algorithm. Experimental results on spam based benchmark dataset reveal that proper setting of feature-weight can improve the performance of the proposed spam filtering approach. Furthermore, the proposed spam filtering ap-proach performance is better than PCM and Naïve Bayes filtering technique.


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Computer Science

How to Cite

Spam Filtering Approach based on Weighted Version of Possibilistic c-Means. (2022). Iraqi Journal of Science, 58(2C), 1112-1127.

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