Frequent and High Utility Itemsets Mining Based on Bi-Objective Evolutionary Algorithm with An Improved Mutation Strategy

Chongyang Li
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Abstract

Frequent and high utility itemsets mining (FHUIM) is one of the important tasks in pattern mining. In order to solve the exponential search space and parameter setting problems that traditional HUIM algorithms encountered, the task of FHUIM was reformulated as a bi-objective problem that can be solved by multi-objective evolutionary algorithms (MOEAs). However, the search efficiency of the MOEAs may become lower when the total distinct items, the number of transactions, and the average length of transactions in the database are larger. To further improve the efficiency of MOEAs for FHUIM, we proposed FHUIM based on bi-objective evolutionary algorithm with an improved mutation strategy (FHUIM-BOEA-IMS). In FHUIM-BOEA-IMS, an improved mutation strategy is proposed to make the items with higher support and utility more likely to be saved in population, by which the FHUIs are more likely to be searched. The results on four popular datasets show that the proposed FHUIM-BOEA-IMS has better performance than the compared baseline in the task of FHUIM in terms of the convergence and final solutions.
基于改进突变策略的双目标进化算法的频繁高效用项集挖掘
频繁高效用项集挖掘是模式挖掘的重要内容之一。为了解决传统混合混合机动算法存在的指数搜索空间和参数设置问题,将混合混合机动任务重新表述为可由多目标进化算法求解的双目标问题。然而,当数据库中不同条目的总数、事务数和平均事务长度较大时,moea的搜索效率可能会降低。为了进一步提高FHUIM的moea效率,我们提出了一种基于改进突变策略的双目标进化算法(FHUIM- boea - ims)。在FHUIM-BOEA-IMS中,提出了一种改进的突变策略,使种群中支持度和效用较高的项目更有可能被保存,从而使fhui更有可能被搜索到。在4个常用数据集上的实验结果表明,所提出的FHUIM- boea - ims在FHUIM任务中的收敛性和最终解均优于对比基线。
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