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Multi-objective Bat Algorithm for Mining Interesting Association Rules

机译：有趣的关联规则挖掘的多目标蝙蝠算法

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摘要

Association rule mining problem attracts the attention of researchers inasmuch to its importance and applications in our world with the fast growth of the stored data. Association rule mining process is computationally very expensive because rules number grows exponentially as items number in the database increases. However, Association rule mining is more complex when we introduce the quality criteria and usefulness to the user. This paper deals with association rule mining issue in which we propose Multi-Objective Bat algorithm for association rules mining Known as MOB-ARM. With the aim of extract more useful and understandable rules. We introduce four quality measures of association rules: Support, Confidence, Comprehensibility, and Interesting-ness in two objective functions considered for maximization. A series of experiments are carried out on several well-known benchmarks in association rule mining field and the performance of our proposal are evaluated and compared with those of other recently published methods including mono-objective and multi-objective approaches. The outcomes show a clear superiority of our proposal in-face-of mono objective methods in terms generated rules number and rule quality. Also, The analysis also shows a competitive outcomes in terms of quality against multi-objective optimization methods.
机译：随着存储数据的快速增长，关联规则挖掘问题引起了研究人员的关注，因为它在我们的世界中具有重要意义和应用。关联规则挖掘过程在计算上非常昂贵，因为规则数量随着数据库中项目数的增加而呈指数增长。但是，当我们向用户介绍质量标准和有用性时，关联规则挖掘会更加复杂。本文针对关联规则挖掘问题，在该问题中，我们提出了用于关联规则挖掘的多目标Bat算法，即MOB-ARM。目的是提取更多有用且易于理解的规则。我们在考虑最大化的两个目标函数中介绍了关联规则的四个质量度量：支持，信心，可理解性和兴趣度。在关联规则挖掘领域的几个知名基准上进行了一系列实验，我们的建议的性能得到了评估，并与其他最近发布的方法（包括单目标和多目标方法）进行了比较。结果表明，就生成的规则数量和规则质量而言，我们的单目标方法面对面建议的优势明显。同样，该分析还显示了与多目标优化方法相比在质量方面具有竞争力的结果。

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