标题: 智慧型知识发现系统
An Intelligent Knowledge Discovery System
作者: 曲衍旭
Yian-Shu Chu
曾宪雄
Dr. Shian-Shyong Tseng
资讯科学与工程研究所
关键字: 知识发现;资料探勘;资料型态转换;专家系统;知识撷取;知识表达;Knowledge Discovery in Database (KDD);Data Mining;Data Types Transformation;Expert System;Knowledge Acquisition;Knowledge Representation
公开日期: 2001
摘要: 近年来,知识发现系统是一个快速成长的研究领域。随着资料量不断快速的增加,如今已经变得很难从各种资料库中找到有用的知识。另外,许多资料探勘
的方法也不断的被发明出来。如此一来使得一般没有资料探勘背景知识的使用者很难去找到适合的资料探勘方法去发现手边资料里面的知识。
在此篇论文中,我们提出一个智慧型知识发现系统的架构(IKDS)来帮助使用者选择适合的资料探勘演算法以及帮助使用者发现知识。另外,我们也提出来一个知识撷取的方法,SEMCUD。利用这个知识撷取的方法不但可以抓出明显的知识,还可以抓出隐含的知识。然后将这些知识用XML去表现以及储存。最后还建制了SEMCUD和IKDS的原型。使用者可以用IKDS的原型去发现知识。
Recently, the knowledge discovery system is a rapidly growing area of research. It is very difficult to discover valid knowledge in the data repositories and is also very difficult to choose suited data mining methods without prior knowledge about data mining or application domain since the amount of raw data becomes large and there are a variety of data mining methods.
In this thesis, we propose a framework of an Intelligent Knowledge Discovery System (IKDS) to help users select appropriate data mining algorithms and discover knowledge. In addition, a knowledge acquisition methodology, SEMCUD, is also proposed to elicit not only explicit knowledge but also implicit knowledge of the experts. The knowledge in IKDS can be represented and stored by XML. The prototypes of SEMCUD and IKDS have been built up to help users discover knowledge.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT900394014
http://hdl.handle.net/11536/68537
显示于类别:Thesis