目標關聯式法則資料探勘演算法

Goal Association Rule

張瑜紋(Yu-Wen Chang)

指導教授 ： 蔣定安

淡江大學/工學院/資訊工程學系碩士在職專班/碩士(2007年)

https://doi.org/10.6846/TKU.2007.00435

摘要

傳統的關聯式法則，是對所有的交易進行分析，將所有交易隱含的關聯找出來，在沒有針對特定目標處理的情況下，往往落入兩種困境，一是處理的時間過長，另外則是產生的關聯法則過多。使用傳統的關聯式法則，過程中需要同時處理龐大的非特定目標，再從中取得特定目標的結果，雖然最後也能取得特定目標的關聯，事實上，決策者可能只對及時的、特定目標發生的關聯有濃厚的興趣，例如：行銷人員於新促銷檔期開始後，觀察消費者對促銷型錄產品購買的行為，以預期銷售額能否達成營業目標。資訊的時效性與準確性，在選擇處理的方式時就已經決定。因此我們提出針對特定目標，進行關聯式法則分析的方式，稱為目標關聯式法則資料探勘演算法(Goal Association Rule，簡稱GAR)。

關鍵字

關聯式法則 ； 支持度 ； 信賴度 ； 資料探勘 ； 概念層級 ； 分類階層

並列摘要

The association rule analysis all transaction in order to find out unknown rules between items. Mining millions transactions without specific target often fail into a corner: one is taking time process, another is more than sufficient rules.Although traditional association rule eventually will obtain association of specific target after dealing with huge unwanted datum. Acturally timely association on specific targets might be most evaluated by decision makers. For example, marketing people observe customer behavior about catalog products during new promotion activity to predict sales amount reaching profit targets.Therefore, we proposed Goal Association Rule algorithm(GAR) aim at specific targets mining desired association rule.

並列關鍵字

Association Rule ； Support ； Confidence ； Data Mining ； Concept Level ； Classification

參考文獻

[3]. S. Brin, R. Motwani, J. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rule for Market Basket Data,” Proceedings of the 1997 SIGMOD Conference on Management of Data, pp. 255-264, 1997.

[5]. J. Hipp, U. Güntzer, and G. Nakhaeizadeh, “Algorithms for Association Rule Mining – A General Survey and Comparison,” SIGKDD Explorations, Vol. 2, Issue 1, pp. 58-64, 2000.

[6]. Osmar R. Zaïane and Maria-Luiza Antonie, “Classifying text documents by associating terms with text categories,” Australian Computer Science Communications ,Vol. 24 , Issue 2,pp. 215- 222, Jan.-Feb. 2002.

[8]. J. Han and Y. Fu, “Mining Multiple-level Association Rules in Large Databases,” IEEE Trans. on Knowledge and Data Eng., Vol. 11. No. 5, pp. 798-805, 1999.

[11]. J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, Dallas, Texas, USA, pp. 1-12, May 2000.