Distributed Frequent Closed Itemsets Mining

Chun Liu, Zheng Zheng, K. Cai, Shichao Zhang
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引用次数: 1

Abstract

As many large organizations have multiple data sources and the scale of dataset becomes larger and larger, it is inevitable to carry out data mining in the distributed environment. In this paper, we address the problem of mining global frequent closed itemsets in distributed environment. A novel algorithm is proposed to obtain global frequent closed itemsets with exact frequency and it is shown that the algorithm can determine all the global frequent closed itemsets. A new data structure is developed to maintain the closed itemsets. Then an efficient implementation is provided based on the structure. Experimental results show that the proposed algorithm is effective.
分布式频繁闭项集挖掘
随着许多大型组织拥有多个数据源,数据集规模越来越大,在分布式环境下进行数据挖掘是不可避免的。本文研究了分布式环境下全局频繁闭项集的挖掘问题。提出了一种求频率精确的全局频繁闭项集的新算法,并证明了该算法能确定所有全局频繁闭项集。开发了一种新的数据结构来维护封闭项集。然后给出了基于该结构的高效实现。实验结果表明,该算法是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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