Using HMT and HASH_TREE to Optimize Apriori Algorithm

2011 International Conference on Business Computing and Global Informatization Pub Date : 2011-07-29 DOI:10.1109/BCGIN.2011.109

Zhiyong Zeng, Hui Yang, Tao Feng

引用次数: 10

Abstract

On the basis of deep analysis to the Apriori algorithm. In this paper, the HMT (HASH MAPPING TABLE) and HASH_TREE methodologies are used to optimize space complexity and time complexity. Using the HMT compressed Item sets, HASH_TREE can decentralize support count process. The result of experimental show that, space complexity and time complexity of Apriori algorithm is Efficiency reduced by using HMT and HASH_TREE.

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利用HMT和HASH_TREE优化Apriori算法

在对Apriori算法进行深入分析的基础上。本文使用HMT (HASH MAPPING TABLE)和HASH_TREE方法来优化空间复杂度和时间复杂度。使用HMT压缩的Item集，HASH_TREE可以分散支持计数过程。实验结果表明，利用HMT和HASH_TREE有效地降低了Apriori算法的空间复杂度和时间复杂度。

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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来源期刊

2011 International Conference on Business Computing and Global Informatization

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