( 您好!臺灣時間:2021/08/03 05:41
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::


研究生(外文):Chu-Chun Hsu
論文名稱(外文):Application of FFP-Growth to Data Mining by Fuzzy Association Rules
外文關鍵詞:Data MiningAssociation RulesFuzzy PartitionFuzzy Frequent Pattern Growth Algorithm
  • 被引用被引用:1
  • 點閱點閱:846
  • 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
資料庫為現今普遍儲存資料的工具,對於如此龐大與激增的資料而言 ,則可運用資料探勘之技術來進行分析、統整,以探勘出有用的資訊,進而形成知識。在資料探勘技術中,是以關聯規則最常見,其主要是從資料庫中找尋項目之間的關聯性。然而在傳統的關聯規則演算法中,需要不斷地重覆檢查資料庫的候選項目集以及掃瞄資料庫,進而無法提升探勘的效率。

本研究以FP-Growth為基礎,並以模糊分割法相結合,提出一項模糊頻率樣式成長(Fuzzy Frequent Pattern Growth, FFP-Growth)演算法。藉由FP-Growth的壓縮資料與掃瞄資料庫兩次之特性,並以模糊分割法來決定於每一項目之模糊集合,來進行關聯規則之探勘。其特色在於交易資料庫經過更新後,完全不須重新掃瞄原始資料庫就可以產生所有的高頻項目組,則可提升探勘之效率。

Database is the common tool for data archive nowadays. It is valuable to obtain useful data and create new knowledge using data mining technologies to analyze and integrate the tremendous and ever-increasing data. The relational rule method which finds relations in database is the most widespread among the data mining technologies. However, it takes repetitive checking and scanning of the database using traditional relational rule algorithm, and consequently limited the data mining efficiency.

This research adopted the FP-Growth as basis and integrated fuzzy partition to propose the Fuzzy Frequent Pattern Growth (FFP-Growth) algorithm that integrated the FP-Growth double database compression and scanning and the fuzzy partition rule to determine the fuzzy group for each item. The feature of this algorithm is that the generate frequent patterns can be created after updating trade database, without re-scanning the original database and therefore improve the data mining efficiency.

The fuzzy association rules were investigated from the quantitative trade data in the third part of the research. In addition, due to the fast increase of internet applications, the useful web browse style was explored from web server browsing records. The explored knowledge can be used in marketing and management decision making, and create new business chances.
書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
表目錄 ix
圖目錄 xi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 研究方法與架構 2
1.5 論文架構 5
第二章 文獻探討 6
2.1 資料探勘 6
2.1.1 資料探勘的崛起 6
2.1.2 資料探勘的定義 7
2.1.3 資料探勘模式 8
2.2 關聯規則挖掘法之相關研究 10
2.2.1 關聯規則定義與相關名詞介紹 10
2.2.2 Apriori 演算法 11
2.2.3 Frequent-pattern growth (FP-growth)演算法 16
2.3 模糊理論 28
2.3.1 模糊資料探勘方式 29
2.3.2 模糊隸屬函數介紹 29
2.3.3 模糊分割 30
第三章 從消費使用模式挖掘模糊交易行為 34
3.1 演算法 34
3.2 範例 35
第四章 從網頁使用模式挖掘模糊瀏覽行為 45
4.1 演算法 45
4.2 範例 47
第五章 結論與未來研究方向 61
5.1 結論 61
5.2 未來研究方向 62
參考文獻 63
[1]Agrawal, R. and Srikant, R., “Fast Algorithm for Mining ssociation Rules,” In Proceedings of the 20th International onference on Very Large Database,1994, pp. 487-499.
[2]Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A. I. (1996), Fast Discovery of Association Rules, in U. M. Fayyad, G. Piatetsky-Shapiro, P., Smyth, and R. Uthurusamy, (Eds.), Advances in Knowledge Discovery and Data Mining, AAAAI Press, Menlo Park 1996, pp.307-328.
[3]A. Ragel, B.Cremilleux, “MVC—a preprocessing method to deal with missing values, ” Data & Knowledge Engineering Volume: 18, Issue: 3 , 1996,pp. 189-223.
[4]Brin, S., Motwani R. and Silverstein, C., “Beyond market baskets ; Generalizing association rules to correlations, ” In Proceedings of ACM SIGMOD Conference on Management of Data, pp.265-276, 1997.
[5]Bellatreche, L., Karlapalem, K., and Mohania, M., “OLAP Query Processing for Partitioned Data Warehouses, ” Proceedings of 1999 International Symposium on Database Applications in Non-Traditional Environments(DANTE ’99) ,1999, pp.35-42.
[6]Bezdek, J. C.(1981), Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, NY, 1981.
[7]Cabena, P., hadjinian, P., stadler, R., Verhees, J. and Zanasi, A., “Discovering Data Mining From Concept to Implementation, ” Prentice-Hall Inc., 1997.
[8]Cheung, D.W., Han, J., Ng, V.T., Fu, A.W. and Fu, Y., “A Fast Distributed Algorithm for Mining Association Rules, ” In Proc. Of 1996 Int’l Conf. on PDIS’96, Miami Beach, Florida, USA, Dec. 1996.
[9]C. H. Lee, C. R. Lin, and M. S. Chen, “Sliding-Window Filtering: An Efficient Algorithm for Incremental Mining”, Proceedings of the ACM 10th International Conference on Information and Knowledge Management, New York, USA November 2001, pp. 263-270,.
[10]Fayyad, U., Piatetsky—Shapiro, G. and Smyth, P., “The KDD process for Extracting Useful Knowledge from Volumes of Data, ” Communications of The ACM, Volume 39, Number11,1996 pp.27-34.
[11]Fu,, Y., “Data mining Tasks, techniques and applications, ” IEEE Potentials, Volume 16, Issue 4, 1997,pp.18-20.
[12]Han, Jiawei and Kamber, Micheline, “Data Mining : Concepts and Techniques” John Wiley & Son,2001.
[13]Hong, T.Z.., Chiang M.J. and Wang S.L.(2008), Mining Fuzzy Weighted Browsing Patterns from Time Duration and with Linguistic Thresholds, American Journal of Applied Sciencses, 2008,5(12), pp.1611-1621.
[14]Hu, Y. C., Chen, R. S. and Tzeng, G. H. (2003), Finding Fuzzy Classification Rules Using Data Mining Techniques, Pattern Recognition Letters, 2003, 24, pp.509-519.
[15]Ishibuchi, H., Nakashima, T., and Murata, T. (1999), Performance Evaluation of Fuzzy Classifier Systems for Multidimensional Pattern Classification Problems, IEEE Transactions on Systems, Man, and Cybernetics, 1999, 29(5), pp.601-618.
[16]J.S. Park, M.S. Chen, and P.S. Yu, “An effective hash based algorithm for mining association rules, ” Proceedings of the ACM SIGMOD International Conference on Management of Data, San Jose, USA,1995, pp.175-186.
[17]J. Han, J. Pei, and X. Yan. "From Sequential Pattern Mining to Structure Pattern Mining:A Pattern-Growth Approach". Journal of Computer Science and Technology, Vol. 19,No. 3, May 2004, pp.257-279.
[18]J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, “H-Mine: Hyper-Structure Mining of Frquent Patterns in Large Databases,” Proceedings of the International Conference on Data Mining, San Jose, CA, November 2001.
[19]M.S. Chen, J. Han and P.S. Yu, “Data Mining:An overview from a database perspective,” IEEE Tramsactions on Knowledge and data Engineering. Vol. 8, No. 6,pp.866-883,1996
[20]R. Srikant and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” Proceedings of the Fifth international Conference on Extending Database Technology, 1996, pp. 3-17.
[21]Takeaki Uno, Taisuya Asai, Yuzo Uchida, Hiroki Arimura “LCM: AN Efficient Algorithm for Enumerating Frequent Closed Item Sets”, In Proc. IEEE ICDM99 Workshop FIMI’03,2003.
[22]T.-P. Hong, K.-Y. Lin, and S.-L. Wang, “Mining fuzzy sequential patterns from multiple- item transactions”, Proceedings of the IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, Vol. 3, Jan. 2001.
[23]Sun, C. T. (1997), Using a Hashed Method with Transaction Trimming and Database Scan Reduction for Mining Association Rules, IEEE Transactions on Knowledge and Engineering, 1997, 19(5), pp.813-825.
[24]Wang, L. X. and Mendel, J. M. (1992), Generating Fuzzy Rules by Learning from Examples, IEEE Transactions on Systems, Man, and Cybernetisc, 1992, 22(6), pp.1414-1427.
[25]Yen John and Reza Langari, Fuzzy Logic Intelligence, Control, andInformation, Prentice-Hall,Inc.,1999.
[26]Yuan Y. and Shaw, M. J. (1995), Induction of Fuzzy Decision Trees, Fuzzy Sets and Systems, 1995, 69, pp.125-139.
[27]Zadeh, L. A. (1965), Fuzzy Sets, Information Control, 1965, 8(3), pp.338-353.
[28]Zadeh, L. A. (1975a), The Concept of a Linguistic Variable and Its Application to Approximate Reasoning, Information Science (part 1), (1975a) , 8(3), pp.199-249.
[29]Zadeh, L. A. (1975b), The Concept of a Linguistic Variable and Its Application to Approximate Reasoning, Information Science (part 2), (1975b), 8(4), pp.301-357.
[30]Zadeh, L. A. (1976), The Concept of a Linguistic Variable and Its Application to Approximate Reasoning, Information Science (part 3), 1976, 9(1), pp.43-80.
[32]王派洲澤,Jiawei Han and Micheline Kamber著,資料探勘概念與方法,滄海書局,民國97年。
[36]胡宜中,使用模糊分割自概念層級架構中找出關聯規則,資訊管理學報,13(3),2006, pp.63-80。
[37]黃仁鵬等,高效率之遞增式資料探勘演算法-ICT,電子商務學報,8(3),2006, pp.393-414。
第一頁 上一頁 下一頁 最後一頁 top