关键词: 频繁项, 数据挖掘, 非线性规划, 遗传算法

Abstract: We describe the problem of frequent item set mining on Boolean variable database as a non-linear optimization problem，which contains the new kind of addition，multiplication and norm.The optimization problem can be summed up to a 0-1 programming，which can be solved by Genetic Algorithm.Based on the analysis in reason of difficulty to problem of frequent item set mining，we present a method to decide the initial population by taking part of the records of original database.We report experimental results showing the implementation of this algorithm with IBM database.The results show that the algorithm will find a passel of frequent items within a few generations.

Key words: frequent item, data mining, nonlinear programming, genetic algorithm