智慧型知識發現系統

Abstract

近年來，知識發現系統是一個快速成長的研究領域。隨著資料量不斷快速的增加，如今已經變得很難從各種資料庫中找到有用的知識。另外，許多資料探勘 的方法也不斷的被發明出來。如此一來使得一般沒有資料探勘背景知識的使用者很難去找到適合的資料探勘方法去發現手邊資料裡面的知識。 在此篇論文中，我們提出一個智慧型知識發現系統的架構(IKDS)來幫助使用者選擇適合的資料探勘演算法以及幫助使用者發現知識。另外，我們也提出來一個知識擷取的方法，SEMCUD。利用這個知識擷取的方法不但可以抓出明顯的知識，還可以抓出隱含的知識。然後將這些知識用XML去表現以及儲存。最後還建製了SEMCUD和IKDS的原型。使用者可以用IKDS的原型去發現知識。

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.