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A Fast Distributed Algorithm for Association Rule Mining Based on Binary Coding Mapping Relation

分类：TP311.13[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]
作者机构：[1]Department of Computer and Engineering,Southeast University,Nanjing 210096, Jiangsu, China, [2]School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu,China
相关基金：Supported by the National Natural Science Foundation of China （70371015）

作者： CHEN Geng[1], NI Wei-wei[1], ZHU Yu-quan[2], SUN Zhi-hui[1]

关键词：数据挖掘, 快速分布式算法, 联合规则挖掘, 二进制数据, frequent itemsets, distributed association rule mining, relation of itemsets-binary data

中文摘要：

协会规则采矿是在数据采矿的一个重要问题。纸建议一个二元(物) 系基于方法高效地产生经常的条款设置的候选人和相应支持计数，它需要仅仅一些操作例如“并且”，“或”并且“ x 或” .Applying 在存在分布式的协会规则采矿算法频分多路复用的这个想法，改进算法 BFDM 被建议。理论分析和实验证明那 BFDM 有效、有效。

英文摘要：

Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only some operations such as ＂and＂, ＂or＂ and ＂xor＂. Applying this idea in the existed distributed association rule mining al gorithm FDM, the improved algorithm BFDM is proposed. The theoretical analysis and experiment testify that BFDM is effective and efficient.

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