Amin Babazadeh Sangar¹, Seyyed Reza Khaze² and Laya Ebrahimi^1
1 Universiti Teknologi Malaysia, Malaysia and ²Islamic Azad University, Iran

Anticipatingthe political behavior of people will be considerable help for election candidates to assess thepossibility of their success and to be acknowledged about the public motivations to select them. In thispaper, we provide a general schematic of the architecture of participation anticipating system inpresidential election by using KNN, Classification Tree and Naïve Bayes and tools orange based on crispwhich had hopeful output. To test and assess the proposed model, we begin to use the case study byselecting 100 qualified persons who attend in 11th presidential election of Islamic republic of Iran andanticipate their participation in Kohkiloye & Boyerahmad. We indicate that KNN can perform anticipationand classification processes with high accuracy in compared with two other algorithms to anticipateparticipation

Anticipating, Data Mining, Naïve Bayes, KNN, Classification Tree