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研究生:曲衍旭
研究生(外文):Yian-Shu Chu
論文名稱:智慧型知識發現系統
論文名稱(外文):An Intelligent Knowledge Discovery System
指導教授:曾憲雄曾憲雄引用關係
指導教授(外文):Shian-Shyong Tseng
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:51
中文關鍵詞:知識發現資料探勘資料型態轉換專家系統知識擷取知識表達
外文關鍵詞:Knowledge Discovery in Database (KDD)Data MiningData Types TransformationExpert SystemKnowledge AcquisitionKnowledge Representation
相關次數:
  • 被引用被引用:3
  • 點閱點閱:696
  • 評分評分:
  • 下載下載:186
  • 收藏至我的研究室書目清單書目收藏:3
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.

Abstract (in Chinese) i
Abstract ii
Acknowledgement iv
Table of Contents v
List of Figures vii
List of Tables viii
List of Algorithms ix
Chapter 1: Introduction 1
Chapter 2: Related Work 4
2.1 Knowledge Discovery in Database 4
2.2 Data Mining 5
2.2.1 Data Warehouse and OLAP 6
2.2.2 Data Types Transformation 7
2.3 Expert System 8
2.3.1 OORBMS 9
Chapter 3: Knowledge Discovery Using Data Mining 11
3.1 Applying Data Mining Technique 11
3.2 Knowledge Discovered in Data Mining 12
3.3 Issues of using Data Mining for Knowledge Discovery 14
Chapter 4: IKDS: The Intelligent Knowledge Discovery System 16
4.1 The First Stage: Construction Stage 18
4.2 The Second Stage: Discovery Stage 19
4.2.1 Inferring and Mining Phase 19
4.2.2 Visualizing and Refining Phase 21
4.2.3 Knowledge Discovery Interaction Unit 22
Chapter 5: Knowledge in IKDS 24
5.1 Knowledge Acquisition 24
5.1.1 EMCUD 25
5.1.2 SEMCUD 25
5.2 Knowledge Representation 32
5.2.1 XML 32
5.2.2 DOM 33
5.2.3 DTD 34
Chapter 6: Implementation 37
6.1 Implementation of SEMCUD 38
6.2 Implementation of IKDS 38
Chapter 7: Conclusions 42
References 43
Appendix A (EMCUD) 49

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