關聯法則是經常被使用在資料探勘的研究技術之一，進而利用關聯規則結合成關聯分類器，預測未知分類資料的類別。

關聯規則執行分類前，會依照演算法所定義的執行順序做排序。一般而言，當規則經過排序後，規則與規則之間的執行先後順序將不再改變。實際上關聯規則執行時，當排序較高的執行後，排序較低未執行的規則在剩餘未分類資料中，可能擁有較原先更高的信賴度，或更低的信賴度，規則間的執行先後順序與重要性可能會有所不同。

因此，本論文對此種情況，利用關聯法則提出多層次排序(Multi-Ranking)分類器，定義關聯分類器的規則執行順序及規則修剪的方法。由實驗結果顯示，多層次排序分類器在預測未知分類能有好的準確率表現與執行效率。

Association rule is one of the .adopted techniques frequently in data mining, then integrating the association rules into a associative classifier for predicting that data are not classified.

The association rules will be sorted by the algorithm’s definition before executing the association rules. In general, between the rule and the rule execution order no longer will change successively when the association rules are sorted. Actually, after executing higher rank , the lower ranking and unexecuted rules will have different confidence from initial confidence in the remaining data. And the rules’ execution order and importance will be difference.

	Therefore, we propose a new classifier named Multi-Ranking classifier in view of the situation, defining the rules of the associative classifier execution orders. Moreover, Multi-Ranking classifier have good accuracy and execution performance in the experiment.

目錄
第一章	1
1.1 前言	1
1.2 研究動機與目的	2
第二章	背景知識及相關研究	10
2.1關聯法則	10
2.2 Apriori演算法	12
2.3 關聯分類法問題	17
2.3.1  CBA演算法	18
2.3.2  L3演算法	22
2.3.3  規則執行順序與修剪	25
第三章	問題陳述及流程圖	28
3.1 問題描述	28
3.2 多層次排序流程圖	35
第四章	實驗結果	38
4.1資料來源及預處理	39
4.2 實驗結果	43
4.2.1原始排序	44
4.2.2多層次排序   47
4.2.3 討論	49
第五章	結論及未來展望	51
參考文獻	52
英文論文        54

圖目錄
圖1.1 關聯規則	4
圖1.2 規則分類前	5
圖1.3 規則分類後	6
圖3.1 信賴度上升	29
圖3.2信賴度下降	31
圖3.3信賴度不變	32
圖3.4 無信賴度	34
圖3.5建置模組及篩選規則流程圖	35

表目錄
表4.1 Breast Cancer Data Set	40
表4.2 LED Display Domain Data Set	41
表4.3 Lymphography Data Set	42
表4.4 初始排序未經修剪	44
表4.5初始排序且經過修剪	46
表4.6 多層次排序	47
表4.7 準確率	49
表4.8 回收率	50

[1].R. Agrawal, T. Imielinski, A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” Proceedings of the 1993 ACM SIGMOD Conferencen on Management of Data, Washington D.C., pp. 207-216, May 1993.
[2] Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993.
[3] Breiman L., J. H. Friedman, R. A. Olshen and C. J. Stone, “Classification and regression trees”, Monterey, CA: Wadsworth, 1984.
[4] G. V. Kass, “An Exploratory Technique for Investigating Large Quantities of Categorical Data”, Applied Statistics, pp.119-127, 1980.
[5] Bing Liu, Wynne Hsu, Yiming Ma, "Integrating Classification and Association Rule Mining," Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98, full paper), New York, USA, 1998.
[6] B. Liu, Y. Ma, and C. K. Wong, Improving an Association Rule Based Classifier., PKDD-2000, 2000, pp. 504-509.
[7] G. Dong, X. Zhang, L. Wong, and, J. Li, CAEP: Classification by aggregating emerging patterns., DS-99 (LNCS 1721), Japan, Dec. 1999.
[8] W. Li, J. Han, and J. Pei, CMAR: Accurate and efficient classification based on multiple class-association rules., ICDM-01, San Jose, CA, Nov. 2001, pp. 369-376.
[9] K. Wang, S. Zhou, Y. He, “Growing decision trees on support-less association rules, ” Knowledge Discovery and Data Mining, pp.265-269, 2000.
[10] E. Baralis, and P. Garza. A lazy approach to pruning classification rules., In Proceedings of the 2002 IEEE International