分类、关联和知识发现——一种使用并行数据挖掘代理(padma)的分布式数据挖掘(DDM)方法

D. Khan
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引用次数: 16

摘要

重点介绍了一种利用多智能体系统(MAS)技术实现分布式数据挖掘(DDM)的方法,提出了一种ldquoCAKErdquo(分类、关联和知识发现)数据挖掘技术。该体系结构基于集中式并行数据挖掘代理(padma)。数据挖掘是一个词的一部分,这个词最近被称为BI或商业智能。需要的是从抽象的数据中得出知识。这个过程困难、复杂、耗时且资源匮乏。建议的模型解决了这些突出的问题。模型体系结构是分布式的,使用知识驱动的挖掘技术,并且足够灵活,可以在任何数据仓库上工作,这将有助于克服这些问题。定义数据挖掘规则需要对数据、元数据和业务领域有良好的了解。考虑到数据和数据仓库已经完成了必要的流程,并为数据挖掘做好了准备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CAKE – Classifying, Associating and Knowledge DiscovEry - An Approach for Distributed Data Mining (DDM) Using PArallel Data Mining Agents (PADMAs)
This paper accentuate an approach of implementing distributed data mining (DDM) using multi-agent system (MAS) technology, and proposes a data mining technique of ldquoCAKErdquo (classifying, associating & knowledge discovery). The architecture is based on centralized parallel data mining agents (PADMAs). Data mining is part of a word, which has been recently introduced known as BI or business intelligence. The need is to derive knowledge out of the abstract data. The process is difficult, complex, time consuming and resource starving. These highlighted problems addressed in the proposed model. The model architecture is distributed, uses knowledge-driven mining technique and flexible enough to work on any data warehouse, which will help to overcome these problems. Good knowledge of data, meta-data and business domain is required for defining rules for data mining. Taking into consideration that the data and data warehouse has already gone through the necessary processes and ready for data mining.
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