基于矩阵改进的关联规则算法研究
Research on Improved Association Rules Algorithm Base on Matrix
DOI: 10.12677/ORF.2019.92017, PDF,  被引量    科研立项经费支持
作者: 朱嘉宏：广州大学数学与信息科学学院，广东 广州 ；谷岩：广州大学网络中心，广东 广州；胡勇军：广州大学工商管理学院，广东 广州
关键词: Apriori算法；矩阵；频繁项集；关联规则；Apriori Algorithm； Matrix； Frequent Itemsets； Association Rules
摘要: Apriori算法是非常经典的关联规则算法。传统的Apriori算法在处理大量数据时运算能力的问题上存在着许多不足，由于其算法本身的特点，循环操作的次数较多，需要不断地对项集进行比对，影响算法的效率。本文基于矩阵改进和优化算法，利用矩阵运算原理，简化运算过程，减少算法的循环次数，提高运行效率，同时减少内存占用。通过本文改进的算法比原来的算法处理数据更为高效。
Abstract: Apriori algorithm is a classical quantitative association rule algorithm. Traditional Apriori algo-rithms have many shortcomings in the computational power of large amounts of data and need to scan the database many times. An improved Apriori algorithm based on compressed matrix re-duced a large number of candidate items and the memory space of data. Experimental results showed that the improved algorithm can mine frequent items effectively and be less spaced than the classical Apriori algorithm.
文章引用：朱嘉宏, 谷岩, 胡勇军. 基于矩阵改进的关联规则算法研究[J]. 运筹与模糊学, 2019, 9(2): 147-155. https://doi.org/10.12677/ORF.2019.92017

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