关联规则挖掘的实用程序:使用Weka工具的案例研究

A. Lekha, C. Srikrishna, Viji Vinod
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引用次数: 8

摘要

本文通过对乳腺癌、蘑菇、喉癌等数据集的案例研究,探讨了使用Weka工具进行关联规则挖掘的实用性。三种关联算法- Apriori, PredictiveApriori和Tertius算法被用来讨论不同的案例研究。并对三种算法进行了比较研究。给出了使用Weka实现数据集关联规则的进一步体系结构。分析表明,虽然这三种算法的实现给出了强关联规则,但它们在生成频繁项集所需的周期数、所需的最小支持、所使用的内存和非数字数据方面存在问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utility of association rule mining: A case study using Weka tool
In this paper a few case studies pertaining to breast cancer, mushroom, larynx cancer and other datasets are studied to find the utility of association rule mining using Weka tool. Three association algorithms - Apriori, PredictiveApriori and Tertius Algorithms are employed to discuss different case studies. A comparative study of the three algorithms is also made. Further architecture for implementing the association rules on datasets using Weka is also given. The analysis reveals that although the implementation of the three algorithms gives the strong association rules they have problems with the number of cycles taken to generate the frequent itemsets, minimum support needed, memory utilized and non-numeric data.
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