1. School of Computer Science and Technology,Fudan University,Shanghai 200433 2.School of Computer Engineering and Science,Shanghai University,Shanghai 200072
Abstract Mining and analysis of transaction sequences provide quantifiable schemes for decision makers to generate sales strategies. By studying the structure of transaction sequence sets according to the commodity sales amount and their variation trend,a kind of growth pattern is defined which reflects the variation trend of commodity price,as well as two methods of similarity measure,shifted window combined distance and angle vector distance,are defined. Based on those definitions,a clustering research is conducted by a goal function with time constraints. The experiments are conducted on the real commodity transaction sequence datasets. The results show that,combined with the growth patterns of two functions,it produces better clustering results under the condition of the time constraint,which could be well explained in practice.
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