基于长度约束的蝙蝠高效用项集挖掘算法

Bat algorithm for high utility itemset mining based on length constraint

为了挖掘满足用户特殊需求,如含指定项目数量的高效用项集(HUI),提出一种基于长度约束的蝙蝠高效用项集挖掘算法(HUIM-LC-BA)。该算法融合蝙蝠算法(BA)和长度约束构建高效用项集挖掘(HUIM)模型,首先将数据库转换为位图矩阵,实现高效的效用计算和数据库扫描;其次,采用重新定义的事务加权效用(RTWU)策略缩减搜索空间;最后,对项集进行长度修剪,使用深度优先搜索和轮盘赌注选择法确定修剪项目。在4个数据集的仿真实验中,当最大长度为6时,与HUIM-BA相比,HUIM-LC-BA挖掘的模式数量分别减少了91%、98%、99%与97%,同时运行时间也少于HUIM-BA;且在不同长度约束条件下,与FHM+(Faster High-utility itemset Ming plus)算法相比运行时间更稳定。实验结果表明,HUIM-LC-BA能有效挖掘具有长度约束的HUI,并减少挖掘模式的数量。

In order to mine the High Utility Itemsets(HUIs)that meet the special needs of users,such as the specified number of items,a Bat Algorithm for High Utility Itemset Mining based on Length Constraint(HUIM-LC-BA)was proposed.By combining the Bat Algorithm(BA)and length constraints,a new High Utility Itemset Mining(HUIM)model was constructed,in which the database was transformed into a bitmap matrix to realize efficient utility calculation and database scanning.Then the search space was reduced by using the Redefined Transaction Weighted Utility(RTWU)strategy.Finally,the lengths of the itemsets were pruned according to the items determined by roulette bet selection method and depth first search.Experiments on four datasets showed that,when the maximum length was 6,the number of patterns mined by HUIM-LC-BA was reduced by 91%,98%,99%and 97%respectively compared with that of HUIM-BA(High Utility Itemset Mining-Bat Algorithm)with less running time;and under different length constraints,the running time of HUIM-LC-BA is more stable compared to the FHM+(Faster High-utility itemset Ming plus)algorithm.Experimental results indicate that HUIM-LC-BA can effectively mine HUIs with length constraints and reduce the number of mined patterns.