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研究生:吳志偉
研究生(外文):Chih-Wei Wu
論文名稱:結合規則挖掘與行為預測來強化網路資訊查詢處理
論文名稱(外文):Using Rule Mining and Behavior Prediction Techniques in Web Information Query Processing
指導教授:何正信何正信引用關係
指導教授(外文):Cheng-Seen Ho
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:資訊工程研究所
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:2001
畢業學年度:89
語文別:中文
論文頁數:70
中文關鍵詞:資料挖掘行為預測案例式推理本體論
外文關鍵詞:Data MiningBehavior PredictionCase-Based ReasoningOntology
相關次數:
  • 被引用被引用:8
  • 點閱點閱:306
  • 評分評分:
  • 下載下載:20
  • 收藏至我的研究室書目清單書目收藏:6
隨著網際網路的盛行,WWW存在著各式各樣的資訊,各種的查詢系統也因應而生。一般的查詢系統的運作方式,皆是由後端的資料庫負責主要的運作,當負載過量時,使用者只能花更多的時間等待系統的服務。本系統提出一個中介於使用者與資料庫之間的網路資訊查詢系統。本系統透過案例式推理,可由以往的案例中推出解答;利用資料挖掘的技術從案例中取得重要的資訊,亦可協助回答查詢;最後,從使用者查詢記錄中,可萃取出相關的行為模式,以之此預測使用者的後續查詢,並預作處理,即可加速並提出滿足使用者所需的資訊。結合上述數種方法,即可在不存取後端資料庫的情況下,滿足使用者的查詢。實驗顯示,本系統確可有效降低資料庫負擔與減少系統的回應時間。
With the widespread popularity of the Internet, there are various kinds of information and data accumulated on the WWW. Thus, it has resulted in the rising and flourishing of different web query systems. The basic operate on pattern of these web query systems mainly relies on the back-end database techniques; however, when the systems are overloaded, the users will have to waste more time waiting for the system services. In this thesis, we provide a web information query system that works as a mediator between users and databases to alleviate the problem. It employs three primary techniques. First, through the method of Case-Based Reasoning, the system can produce possible, adapted solutions from former cases. Next, by using the technique of Data Mining, the system can learn useful rules from previous cases to make deeper rule-based reasoning about solutions. Finally, from the chronicle records of users’ past web queries, we can extract relative modes of users’ behaviors in order to predict their future queries. With the combination of all these above-mentioned techniques, we can accelerate the processing speed and bring up user-satisfying information without making a single access to the rear-end database. As we can see from the experiments, the system can moderate the loading of databases and can reduce the response time of the given queries.
第一章 緒論1
1.1 背景1
1.2 動機1
1.3 目標2
1.4 資訊管理代理人3
1.5 相關研究5
1.6 論文架構5
第二章 基礎技術與技術7
2.1 本體論7
2.1.1 PCDIY本體論7
2.1.2 Problem本體論11
2.2 案例式推理12
2.3 資料挖掘13
2.4 產生頻繁項目集15
2.4.1 Apriori演算法15
2.4.1.1 apriori-gen演算法20
2.4.2 Eclat 演算法22
2.4.3 混合演算法26
2.5 產生關聯規則27
2.5.1 m-genrule演算法29
2.5.2 ap-genrules演算法31
2.6 挖掘循序樣式33
第三章 系統架構37
3.1 系統概述37
3.2 Interface Agent40
3.2.1 Internal Query Format41
3.2.2 回饋機制42
3.3 Solution Finder43
3.3.1 Predicted Solution Retrieval43
3.3.2 Case-Based Reasoning44
3.3.3 Rule-Based Reasoning51
3.3.4 Solution Integrator51
3.4 Rule Miner52
3.5 Query Predictor58
第四章 實驗60
4.1 Predicted Solution Retrieval60
4.2 CBR和RBR處理問題百分比61
第五章 結論與未來發展63
5.1 結論63
5.2 未來發展64
參考文獻65
中英對照表68
作者簡介70
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