Many scholars have made some achievements in aggregation analysis of terrorist events by using the data set of "Global Terrorism Research Database"(GTD) with game theory, k nearest neighbor method and support vector machine. However, data sparsity and high dimensional multi redundancy are not well considered in the previous research, which may lead to low accuracy of clustering classification. This paper proposes a TFM classification model based on "Minimal redundancy maximal relevancy" (mRMR) combined with " Factorization Machines " (FM), in which the incremental search method is used to find approximately optimal features to address the high dimensional multi redundancy and the data sparsity is tackled with FM method. TFM model is then used to make quantitative classification on the pre processed terrorist attack data. The experimental results show the proposed TFM model, in terms of Matthews correlation coefficient (MCC), is increased by 49.9%, 2.5% and 2.3% respectively compared with naive Bayes (NB), support vector machine (SVM) and logistic regression (LR). The comparative result demonstrates that TFM model is feasible to some extent.
