Apriori、Eclat和FP-Growth频繁项集挖掘算法的数据集特征比较
Comparing Dataset Characteristics that Favor the Apriori, Eclat or FP-Growth Frequent Itemset Mining Algorithms
摘要：
频繁项集挖掘是一种流行的数据采掘技术。Apriori、Eclat和FP-Growth是最常用的频繁项集挖掘算法。已经进行了相当多的研究来比较这三种算法之间的相对性能，通过评估每个算法的可扩展性，随着数据集大小的增加。虽然随着数据大小的增加可扩展性很重要，但以前的论文没有研究包含不同项集特征的类似大小的数据集的性能影响。本文探讨了这三个频繁项集算法的性能，两个数据集的特点可以有影响。为了进行这种实证分析，创建了一个数据集生成器来测量频繁项密度和最大事务大小对性能的影响。生成的数据集包含相同数量的行。这提供了对有助于每个算法的数据集特征的一些洞察。本文的研究结果表明，Eclat和FP-Growth都处理的最大事务规模和频繁项集密度的增加，大大优于Apriori算法。本文探讨了这三个频繁项集算法的性能，两个数据集的特点可以有影响。为了进行这种实证分析，创建了一个数据集生成器来测量频繁项密度和最大事务大小对性能的影响。生成的数据集包含相同数量的行。这提供了对有助于每个算法的数据集特征的一些洞察。本文的研究结果表明，Eclat和FP-Growth都处理的最大事务规模和频繁项集密度的增加，大大优于Apriori算法。
英文摘要：
Frequent itemset mining is a popular data mining technique. Apriori, Eclat, and FP-Growth are among the most common algorithms for frequent itemset mining. Considerable research has been performed to compare the relative performance between these three algorithms, by evaluating the scalability of each algorithm as the dataset size increases. While scalability as data size increases is important, previous papers have not examined the performance impact of similarly sized datasets that contain different itemset characteristics. This paper explores the effects that two dataset characteristics can have on the performance of these three frequent itemset algorithms. To perform this empirical analysis, a dataset generator is created to measure the effects of frequent item density and the maximum transaction size on performance. The generated datasets contain the same number of rows. This provides some insight into dataset characteristics that are conducive to each algorithm. The results of this paper's research demonstrate Eclat and FP-Growth both handle increases in maximum transaction size and frequent itemset density considerably better than the Apriori algorithm. This paper explores the effects that two dataset characteristics can have on the performance of these three frequent itemset algorithms. To perform this empirical analysis, a dataset generator is created to measure the effects of frequent item density and the maximum transaction size on performance. The generated datasets contain the same number of rows. This provides some insight into dataset characteristics that are conducive to each algorithm. The results of this paper's research demonstrate Eclat and FP-Growth both handle increases in maximum transaction size and frequent itemset density considerably better than the Apriori algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
