博碩士論文 102423027 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:104 、訪客IP:18.207.136.189
姓名 洪家育(Jia-Yu Hong)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱
(Noise free Attribute oriented induction)
相關論文
★ 零售業商業智慧之探討★ 有線電話通話異常偵測系統之建置
★ 資料探勘技術運用於在學成績與學測成果分析 -以高職餐飲管理科為例★ 利用資料採礦技術提昇財富管理效益 -以個案銀行為主
★ 晶圓製造良率模式之評比與分析-以國內某DRAM廠為例★ 商業智慧分析運用於學生成績之研究
★ 運用資料探勘技術建構國小高年級學生學業成就之預測模式★ 應用資料探勘技術建立機車貸款風險評估模式之研究-以A公司為例
★ 績效指標評估研究應用於提升研發設計品質保證★ 基於文字履歷及人格特質應用機械學習改善錄用品質
★ 以關係基因演算法為基礎之一般性架構解決包含限制處理之集合切割問題★ 關聯式資料庫之廣義知識探勘
★ 考量屬性值取得延遲的決策樹建構★ 從序列資料中找尋偏好圖的方法 - 應用於群體排名問題
★ 利用分割式分群演算法找共識群解群體決策問題★ 以新奇的方法有序共識群應用於群體決策問題
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 屬性導向歸納方法(簡稱AOI方法)主要是被發展來挖掘關連式資料庫的一般化知識,這種方法的輸入包括一個關連式資料表和一組與資料表屬性相關的概念階層 (或稱為概念樹) 。它是一種以歸納為基礎的資料分析技術,將關聯式表格 (Relational Dataset) 資料集合中的每一個屬性,檢查其資料分佈,以決定應歸納到哪個相關的抽象層級。但是因為屬性導向歸納方法很容易受到干擾值 (noise) 的影響,使得歸納出的結果的一般化特徵過於粗略。對於此問題,本論文提出一個以AOI方法為基礎的Noise-free AOI方法,此演算法能將資料中的干擾值(Noise data)過濾掉,讓屬性導向歸納法找出的一般化特徵更加明確。
摘要(英) Attribute oriented induction ( AOI for short) was developed mainly to mine generalized knowledge of relational dataset, this approach include a relational dataset and a set of attributes associated with concept (or concept tree). It is a kind of generalize -based data analysis techniques, and the relational Dataset in each of the properties, checking its data distribution to determine which should be grouped into relevant levels of abstraction. But attribute oriented induction method is very susceptible to interference noise effects, so the results of the generalization features too sketchy. For this problem, this paper proposes a method based AOI, is Noise-free AOI methods. This algorithm can filter out the noise data, so that Noise free AOI can generalize more clearly.
關鍵字(中) ★ 屬性導向歸納法
★ 概念階層
★ 關連式資料
★ 資料挖礦
★ 干擾值
關鍵字(英) ★ attribute oriented induction
★ concept tree
★ relation dataset
★ data mining
★ noise data
論文目次 目錄
圖目錄 2
表目錄 3
第一章、簡介 6
1.1 屬性導向歸納方法簡介 6
1.2 研究動機 7
1.3 研究目的 9
第二章、文獻探討 10
2.1 屬性導向歸納背景介紹 11
2.2 提升屬性導向歸納效率的方法 15
2.3 解決傳統屬性導向歸納使用上的問題 15
2.4 屬性導向歸納模糊概念層級階層的應用 15
2.5 以基本的屬性導向歸納方法為基礎進行擴充 16
2.6 整合其他 AOI 的應用 16
第三章、問題定義 17
第四章、演算法 24
4.1 Algorithm 1: 24
4.2 Algorithm 2: 25
第五章、實驗 27
第六章、結論 44
第七章、參考文獻 44
附錄A 48
附錄B 50
參考文獻 [1] Cai, Y. (1989). Attribute-oriented induction in relational databases (Doctoral dissertation, Simon Fraser University).
[2] Carter, C. L., & Hamilton, H. J. (1998). Efficient attribute-oriented generalization for knowledge discovery from large databases. Knowledge and Data Engineering, IEEE Transactions on, 10(2), 193-208.
[3] Cheung, D. W., Hwang, H. Y., Fu, A. W., & Han, J. (2000). Efficient rule-based attribute-oriented induction for data mining. Journal of Intelligent Information Systems, 15(2), 175-200.
[4] Huang, S. M. (2013). CFAOI: Concept-Free AOI on Multi Value Attributes. Life Science Journal, 10(4).
[5] Hsu, C. C. (2004). Extending attribute-oriented induction algorithm for major values and numeric values. Expert Systems with Applications, 27(2), 187-202.
[6] Yager, R. R., & Petry, F. E. (2006). A multicriteria approach to data summarization using concept ontologies. Fuzzy Systems, IEEE Transactions on, 14(6), 767-780.
[7] Angryk, R. A., & Petry, F. E. (2006). Knowledge discovery in fuzzy databases using attribute-oriented induction. In Foundations and Novel Approaches in Data Mining (pp. 169-196). Springer Berlin Heidelberg.
[8] Lee, K. M. (2001, July). Mining generalized fuzzy quantitative association rules with fuzzy generalization hierarchies. In IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th (pp. 2977-2982). IEEE.
[9] Knorr, E. M., & Ng, R. T. (1996, August). Extraction of Spatial Proximity Patterns by Concept Generalization. In KDD (pp. 347-350).
[10] Chen, Y. L., & Shen, C. C. (2005). Mining generalized knowledge from ordered data through attribute-oriented induction techniques. European Journal of Operational Research, 166(1), 221-245.
[11] Muyeba, M., Crockett, K., & Keane, J. (2011). A hybrid interestingness heuristic approach for attribute-oriented mining. In Agent and Multi-Agent Systems: Technologies and Applications (pp. 414-424). Springer Berlin Heidelberg.
[12] Knorr, E. M., & Ng, R. T. (1996, August). Extraction of Spatial Proximity Patterns by Concept Generalization. In KDD (pp. 347-350).
[13] Wang, L. Z., Zhou, L. H., & Chen, T. (2004, August). A new method of attribute-oriented spatial generalization. In Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on (Vol. 3, pp. 1393-1398). IEEE.
[14] Lu, W., Han, J., & Ooi, B. C. (1993, June). Discovery of general knowledge in large spatial databases. In Proc. Far East Workshop on Geographic Information Systems, Singapore (pp. 275-289).
[15] Liu, Q. H., Tang, C. J., Li, C., Liu, Q. W., Zeng, T., & Jiang, Y. G. (2007). Traditional Chinese Medicine prescription mining based on attribute-oriented relevancy induction. Jisuanji Yingyong/ Journal of Computer Applications,27(2), 449-452.
[16] Tsumoto, S. (2000). Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic. Information Sciences, 124(1), 125-137.
[17] Al-Mamory, S. O., & Zhang, H. (2009). Intrusion detection alarms reduction using root cause analysis and clustering. Computer Communications, 32(2), 419-430.
[18] Julisch, K. (2003). Clustering intrusion detection alarms to support root cause analysis. ACM Transactions on Information and System Security (TISSEC),6(4), 443-471.
[19] Li, S. T., Shue, L. Y., & Lee, S. F. (2008). Business intelligence approach to supporting strategy-making of ISP service management. Expert Systems with Applications, 35(3), 739-754.
[20] Sun, J., & Li, H. (2008). Data mining method for listed companies’ financial distress prediction. Knowledge-Based Systems, 21(1), 1-5.
[21] Zhu, X. D., & Huang, Z. Q. (2008). Conceptual modeling rules extracting for data streams. Knowledge-Based Systems, 21(8), 934-940.
[22] Xu, Z. (2008). On multi-period multi-attribute decision making. Knowledge-Based Systems, 21(2), 164-171.
[23] X. Zhu, Z. Huang, Conceptual modeling rules extracting for data streams, Knowledge-Based Systems 21 (8) (2008) 934–940.
[24] Z. Xu, On multi-period multi-attribute decision making, Knowledge-Based Systems 21 (2) (2008) 164–171.
指導教授 陳彥良(Yen-Liang Chen) 審核日期 2015-7-27
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明