关键词: 二次聚类, 惩罚聚类, 隐马尔可夫链, 持卡消费, 行为预测

Abstract: Bank card payments account for a large proportion in the social consumption, which plays a major role in the promotion of economic growth. So, predicting consumer behavior is important. However, the traditional methods are difficult to effectively deal with complex data and dynamic changes. Based on this, a customer behavior prediction method for card consumption based on two-step clustering and Hidden Markov Chain (HMC) was presented. Firstly, consumer behaviors were conduced by pattern clustering based on sequence; then the secondary clustering was conducted by introducing penalty clustering, which carried out the equilibrium division of the hierarchical states in the sequential pattern. Secondly, HMC was used to estimate the state transition of consumption levels in the sequence and predict the future consumer behavior of the users. Finally, the experimental comparison and analysis results on the traditional clustering, clustering without penalty and clustering with penalty show that the proposed method based on two-step clustering and HMC is more suitable to the consumer behavior prediction model.

Key words: two-step clustering, penalty clustering, Hidden Markov Chain (HMC), card consumption, behavior prediction