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研究生:蘇伯達
研究生(外文):Po-Ta Su
論文名稱:以螞蟻系統探索關聯性規則
論文名稱(外文):Mining Association Rules by Ant System
指導教授:王小璠王小璠引用關係
指導教授(外文):Hsiao-Fan Wang
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
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:49
中文關鍵詞:資料挖礦關聯性規則螞蟻系統演算法
外文關鍵詞:Data MiningAssociation RulesAnt SystemsAlgorithms
相關次數:
  • 被引用被引用:5
  • 點閱點閱:215
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
以螞蟻系統探索大型資料庫, 可以用來發覺相關性的模型, 許多公司以發覺關聯性規則的方式, 來了解不同產品之間的關聯性, 以更進一步的改善客戶服務並擴大市場. 近來, 有許多的演算法提出, 分別對值化及量化資料加以演算, 而螞蟻系統則可以同時考慮這兩種資料型態, 以有效率並可靠的方式演算出結果,以提供決策者有效的決策支援. 在本篇論文中, 我們應用並提出相關性質與分析螞蟻系統建立關連項目及其強度, 進而了解其正確性,穩定性與複雜度, 同時以一示例說明之.
Mining association rules is to find relations among large amount of data so that the pattern of the dataset can be discovered. Many companies use association rules to find the relations among different items to improve their service quality of customers or enlarge their marketplace. Recently, many algorithms have been developed that only consider either non-quantitative data or quantitative data. However, in reality, most data we collected are mixed in types. Since Ant System allows to consider both of data types and has advantages of being efficient in filtering the unobvious association rules to reduce the unnecessary outputs and ease of making judgment to improve the performance, therefore, in this study, we adopted the technique and concept of Ant System to develop association rules. The developed algorithm is supported by theoretical evidence, and comparative studies are provided for evaluation.
摘 要 ......................i
ABSTRACT...................ii
ACKNOWLEDGEMENT ...........iii
CONTENTS...................iv
TABLE AND FIGURE CAPTIONS ...v
LIST OF NOTATIONS..........vi
Chapter 1 INTRODUCTION ......1
Chapter 2 LITERATURE REVIEW..6
2-1. Association Rules ......6
2-2. An Ant System ..........9
Chapter 3 THE PROPOSED METHOD FOR DISCOVERING ASSOCIATION RULES...................13
3-1. Properties of the Association Rules ........13
3-2. The Algorithm...............................14
3-3. Evaluation and Discussion ..................19
IV. NUMERICAL ILLUSTRATION......................24
4-1. Numerical Example......................24
4-2. Efficiency and Reliability.............26
4-2-1. Accuracy of Proposed Method.............26
4-2-2. Stability of Proposed Method.............28
V. SUMMERY AND CONCLUSION......................31
REFERENCES...............................32
APPENDIX........................................34
Appendix A. Unsupervised Result of Proposed Ant System
...............................34
Appendix B. Proposed Ant System Program...35
[1] R.Agrawal and R.Srikant, ”Fast Algorithm for Mining Association Rules in Large Database,” Proc. 20th Int’l Conf. Very Large Data Bases, Santiago, Chile, pp. 478-499,1994.
[2] Nuansri Denwattana and Juanusz R Getta, “A Parameterised Algorithm for Mining Association Rules,” Data Conference , 2001. ADC 2001, Proceedings, 12th Australasian, 2001, pp.45-51, 2001.
[3] Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni, “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Transaction on Systems, Man, and Cybernetics, Vol. 26, No.1, pp. 29-41, February 1996.
[4] Marco Dorigo and Luca Maria Gambardella, “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem,” IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp. 53-66, April 1997.
[5] Tzung-Pei Hong, Tzu-Ting Wang and Been-Chian Chien, “Mining Approximate Coverage Rules,” International Journal of Fuzzy Systems, Vol. 3, Vol. 2, pp. 409-414, june 2001.
[6] Yan-Chia Liang and Alice E. Smith, “An Ant System Approach to Redundancy Allocation,” Evolutionary Computation, 1999, CEC 99, Proceeding of the 1999 Congress on, pp. 1478-1484, 1999.
[7] Vittorio Maniezzo and Antonella Carbonaro, “An Ants Heuristic for the Frequency Assignment Problem,” Future Generation Computer System 16, pp. 927-935, 2000.
[8] Lawrence J. Mazlack, “Granulation of Quantitative Association Rules,” International Journal of Fuzzy Systems, Vol. 3, No. 2, pp. 400-408, june 2001.
[9] Biggs Norman L., Lloyd E. Keith and Wilson Robin J. /Clarendon, Graph theory, 1736-1936, 1986.
[10] Peter R. Peacock , “Data Mining in Marketing : Part 1,” Marketing Management, pp.9-18, 1998
[11] Peter R. Peacock , “Data Mining in Marketing : Part 2,” Marketing Management, pp.15-25, 1998.
[12] R. Andrew Russell, “Ant Trails — an Example For Robots to Follow? ,” Proceedings of the 1999 IEEE International Conference on Robotics & Automation Detroit, Michigan, pp.2698-2703, May 1999.
[13] E.-G. Talbi, O. Roux, C. Fonlupt, D. Robillard, “Parallel Ant Colonies for the Quadratic Assignment Problem,” Future Generation Computer System 17, pp. 441-449, 2001.
[14] Shin-mu Tseng, “Mining association Rules with Interesting Constraints in Large Database,” International Journal of Fuzzy System, Vol.3, No.2, pp. 415-421, June 2001.
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