Algorithm Based on Bit Objects for Mining Maximal Frequent Patterns
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摘要: 提出了基于位对象的最大频繁模式挖掘算法.算法中,用位对象表示数据,并用位对象概念改进FP-Tree.用深度优先搜索策略,通过压缩数据库,并用位对象的特性简化模式支持度的计数,使挖掘时不需产生条件FP-Tree和候选项目集,以提高最大频繁模式的挖掘效率.实验结果验证了BFP-Miner的有效性.Abstract: A new algorithm based on bit objects,BFP-Miner,for mining maximal frequent patterns was proposed.It uses the bit objects to express data and to improve the FP-Tree(frequent pattern tree).The algorithm uses depth-first search strategy,and simplifies the support counting of frequent patterns with the characteristics of the bit objects and by compression of the database.Neither a conditional FP-Tree nor candidate patterns are generated during mining the maximal frequent patterns,so that the mining efficiency is increased.Experimental result verifies the efficiency of the BFP-Miner.
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Key words:
- data mining /
- association rule /
- maximal frequent pattern /
- bit object
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