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首页> 外文会议>Proceedings of the 5th International Conference on System Modeling amp; Advancement in Research Trends >Adaptive Apriori Algorithm for frequent itemset mining
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Adaptive Apriori Algorithm for frequent itemset mining

机译：频繁项集挖掘的自适应Apriori算法

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摘要

Obtaining frequent itemsets from the dataset is one of the most promising area of data mining. The Apriori algorithm is one of the most important algorithm for obtaining frequent itemsets from the dataset. But the algorithm fails in terms of time required as well as number of database scans. Hence a new improved version of Apriori is proposed in this paper which is efficient in terms of time required as well as number of database scans than the Apriori algorithm. It is well known that the size of the database for defining candidates has great effect on running time and memory need. We presented experimental results, showing that the proposed algorithm always outperform Apriori. To evaluate the performance of the proposed algorithm, we have tested it on Turkey student's database as well as a real time dataset.
机译：从数据集中获取频繁项集是数据挖掘中最有前途的领域之一。 Apriori算法是从数据集中获取频繁项集的最重要算法之一。但是，该算法在所需时间以及数据库扫描次数方面均失败。因此，本文提出了一种新的Apriori改进版本，与Apriori算法相比，该方法在所需时间和数据库扫描次数方面均十分有效。众所周知，用于定义候选对象的数据库的大小对运行时间和内存需求有很大影响。我们提供了实验结果，表明所提出的算法始终优于Apriori。为了评估该算法的性能，我们已经在土耳其学生数据库和实时数据集上对其进行了测试。

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