College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China

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Abstract—Aim at the faults of Dynamic Clustering Algorithm based on Fuzzy Equation Matrix, we raise a fuzzy clustering algorithm based on factor analysis, which it combines the technology of reducing dimension using factor analyses method. The algorithm will deal with the sample collections before fuzzy clustering, which enlarge the scale of using dynamic clustering algorithm to resolve practical problems. All these show that the algorithm has a strong capability of concluding and abstracting through being applied to E-mail filtering. At the same time, we also make an experiment in our optional database. The experiment result verifies that the algorithm recall rate is 87.3 % in the mail filtering, which is higher than the SVM’s 80.1%, Naïve Bayes’s 61.7%, and KNN’s 73.2% respectively. The experiments show that the new algorithm has better recall rate and error rate.

Index Terms—factor analysis, fuzzy clustering, fuzzy equivalence relation, Spam Filtering

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Cite: Jingtao Sun, Qiuyu Zhang, and Zhanting Yuan, "Fuzzy Clustering Algorithm based on Factor Analysis and its Application to Mail Filtering," Journal of Software vol. 4, no. 1, pp. 58-64, 2009.