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首页> 外文期刊>International Journal of Systems and Service-Oriented Engineering >Multilevel Clustering of Induction Rules: Application on Scalable Cognitive Agent
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Multilevel Clustering of Induction Rules: Application on Scalable Cognitive Agent

机译：归纳规则的多层聚类：在可扩展认知主体上的应用

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摘要

The tremendous size of data in nowadays world web invokes many data mining techniques. The recent emergence of some new data mining techniques provide also many interesting induction rules. So, it's important to process these induction rules in order to extract some new strong patterns called meta-rules. This work explores this concept by proposing a new support for induction rules clustering. Besides, a new clustering approach based on multilevel paradigm called multilevel clustering is developed for the purpose of treating large scale knowledge sets. The approach invokes k-means algorithm to cluster induction rules using new designed similarity measures. The developed module have been implemented in the core of the cognitive agent, in order to speed up its reasoning. This new architecture called Multilevel Miner Intelligent Agent (MMIA) is tested on four public benchmarks that contain 25000 rules, and compared to the classical one. As foreseeable, the multilevel clustering outperforms clearly the basic k-means algorithm on both the execution time and success rate criteria.
机译：当今世界网络中庞大的数据量引发了许多数据挖掘技术。最近出现的一些新数据挖掘技术也提供了许多有趣的归纳规则。因此，处理这些归纳规则对于提取称为元规则的一些新的强模式很重要。这项工作通过提出对归纳规则聚类的新支持来探索这一概念。此外，为了处理大规模知识集，开发了一种基于多级范式的新聚类方法，称为多级聚类。该方法使用新设计的相似性度量调用k-means算法对归纳规则进行聚类。已开发的模块已在认知主体的核心中实施，以加快其推理速度。这种称为多级矿工智能代理（MMIA）的新体系结构已在包含25000条规则的四个公共基准上进行了测试，并与传统的基准进行了比较。可以预见，在执行时间和成功率标准上，多层聚类明显优于基本的k-means算法。

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