Consumption Pattern Recognition System Based on SVM

In this paper, we present a consumption pattern recognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system, three dimension reduction methods including Principal Component Analysis (PCA), correlation analysis and data cubes are applied to reduce dimension of features and two training methods including Support Vector Machine (SVM) and Support Vector Machine by Increasing Negative Examples (SVM-INE) are utilized to build classifiers. Consumption pattern recognition system can find the consumption habits of specific consumer group which are helpful to well-targeted marketing. Empirical results show that the system can recognize different consumption pattern with high efficiency and accuracy.