臺灣博碩士論文加值系統

本研究以多商店環境與多支持度門檻進行頻繁樣式探勘，許多研究不論是在關聯規則（association rule）還是頻繁樣式（frequent pattern）都面臨探勘結果精準度與效率的問題。Hu & Chen（2006）提出CPF-growth演算法進行多重門檻值的頻繁樣式探勘，Tang et al.（2008）提出多商店環境進行關聯規則的探勘，都是針對企產面臨到的狀況。企業面臨的不是單一面向的問題，是由許多不同面向構成的複雜問題。本研究所提出的方法將在短時間取得更精確的資訊與知識，滿足企業內不同層級的決策需求。

In this study, multi-store and multi-environment support threshold frequent pattern mining, many studies both in the association rule or frequent pattern are faced with the results of exploration accuracy and efficiency. Hu & Chen（2006）proposed CPF-growth algorithm for frequent pattern mining Multiple minimun support, Tang et al.（2008）proposed multi-store environment mining association rules, Both are facing the situation for enterprises to produce. Not a single issue-oriented enterprises are facing a complex problem facing many different configuration. The method proposed in this study will be to obtain more precise information and knowledge in a short time, to meet the needs of different levels of decision-making within the enterprise.

表目錄 ix
圖目錄 xi
第壹章 緒論 1
第貳章 文獻探討 7
第一節　　Apriori 7
第二節　　FP-growth 10
第三節　　Multi store mining 13
第四節　　CFP-growth 18
第參章 重要定義 19
第一節　　MIS value 22
第二節　　Place and Time 25
第三節　　Section And TP lattice 27
第四節　　SIT table and FPT 29
第肆章 重要概念 35
第一節　　MIS-tree 35
第二節　　CFP-growth 42
第伍章 研究方法 51
第一節　　合併Trans_tree 51
第二節　　建立FPT 63
第三節　　NFP-growth 72
第陸章 實驗設計 79
第一節　　實驗資料 79
第二節　　CFP與OCFP 80
第三節　　OCFP與NFP 84
第四節　　多商店環境探勘 88
第五節　　因時、地制定的多商店環境探勘 93
第柒章 結論與未來展望 99
第一節　　研究結論 99
第二節　　學術貢獻 100
第三節　　研究限制與未來研究方向 100
參考文獻 103

一、中文文獻
1.洪冠群，多重最小支持度關聯規則探勘演算法之醫療檢驗應用：以血液透析病患之住院預測為例，國立東華大學資訊工程學系碩士在職專班論文，2004。
2.黃文璋，統計思維，中央研究院數學研究所數學傳播，第33卷第3期，2009，頁30-46。
二、英文文獻
3.Agrawal, R. & Srikant, R., Fast Algorithms for Mining Association Rules, Proceedings of the 20th Very Large DataBases Conference（VLDB’94）, Chili, Santiago, 1994.
4.Borgwardt, K. M., Kriegel, H. P. & Wackersreuther, P., Pattern Mining in Frequent Dynamic Subgraphs, Sixth International Conference on Data mining, 2006.
5.Chen, Y. L., Tang, K., Shen, R. J. & Hu, Y. H., Market basket analysis in a multiple store environment, Decision Support Systems 40, 2005, pp339-354.
6.Cheng, H., Yan, X., Han, J. & Hsu, C. W., Discriminative Frequent Pattern Analysis for Effective Classification, IEEE 23rd International Conference on Data Engineering, 2007.
7.Han, J., Pei, J. & Y. Yin, Mining frequent patterns without candidate generation, Proceedings 2000 ACM-SIGMOD International Conference on Management of Data （SIG-MOD’00）, Dallas, TX, USA, 2000.
8.Hu, Y. H. & Chen, Y. L., Mining association rules with multiple minimum supports: a new minig algorithm and a support tuning mechanism, Decision Support Systems 42, 2006, pp. 1-24.
9.Huang, J. W., Dai, B. R. &Chen, M. S.,Twain: Two-end association miner whith precise frequent exhibition periods, ACM Transactions on Knowledge Discovery from Data, Data. 1, 2, Article 8, 2007.
10.IDC, The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things, 2014, Retrieved 2016/8/30 from http://www.emc.com/leadership/digital-universe/2014iview/index.htm
11.Isafiade, O., Bagula, A. & Berman, S., A Revised Frequent Pattern Model for Crime Situation Recognition Based on Floor-Ceil Quartile Function, Procedia Computer Science 55, 2015, pp. 251-560.
12.Jea, K. F. & Li, C. W., Discovering frequent itemsets over transactional data streams through an efficient and stable approximate approach, Expert Systems with Applications 36, 2009, pp.12323-12331.
13.Li, H. F. & Lee, S. Y., Approximate mining of maximal frequent itemset in data streams with different window models, Expert Systems with Applications 35, 2008, pp.781-789.
14.Li, C. W. & Jea, K. F., An adaptive approximation method to discover frequent itemsets over sliding-window-based data streams, Expert Systems with Applications 38, 2011, pp.13386-13404.
15.Liu, B., Hsu, W. & Ma, Y., Mining association rules with multiple minimum supports, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining （KDD-99）, San Diego, CA, USA, 1999.
16.Tang, K., Chen, Y. L. & Hu, H.W., Context based market basket analysis in a multiple store environment, Decision Support Systems 45, 2008, pp. 150-163.
17.Zhang, S., Zhang, J. & Jin, Z.,A decremental algorithm of frequent itemset maintenance for mining updated databases, Expert Systems with Applications 36, 2009, pp.10890-10895.