臺灣博碩士論文加值系統

目前關聯規則探勘的應用與研究中，以交易所產生的產品群集為對象討論居多，並且在探勘過程及產出的表示上，沒有一個通則性的表示方法，通常以獨特的方法或是文字描述。本研究提出一個概念經由將交易行為的參與者作為關聯規則探勘的對象。讓關聯規則探勘應用更為靈活，配合集合方法論以實體關聯圖形為延伸的一個表示法，讓無論使用何種方法實作下，都可以簡潔而明確的表達，並且充分描述關聯規則探勘的各種限制，無論實體關聯結構、星狀結構、雪花結構，都可加以描述，還可以作階層歸納，及描述不同歸納階層之間的關係。另可以經由指定探勘對象，使用不同交易對象探勘，取得更多樣的探勘內涵。

At present the application of association rule mining and research, to exchange products generated discussion targeted mostly clustered, and in the exploration process and output that, there is no a general rule of representation, usually in a unique way or the text description . This study proposes a concept of transactions by participants in the association rule mining as an object. For association rule mining applications more flexible, to entities associated with the set methodology for the extension of a graphical representation, so that regardless of the implementation of the method, the can be simple and clear expression, and association rule mining to fully describe the various restrictions , regardless of entity-relationship structure, star structure, snow structure, can be described as a class can be summarized, and describe the relationship between different induction levels. Another object via the specified mining, exploration using different trading partners, meaning more like mining.

摘要 I
ABSTRACT II
目錄 III
圖目錄 V
表目錄 IX
第一章　緒論 1
1.1　研究背景 2
1.2　研究動機 3
1.3　研究目地 4
第二章　文獻探討 5
2.1　集合 5
2.2　集族 5
2.3　實體關係圖 6
2.4　延伸式實體關係圖 9
2.5　資料探勘 13
2.6　概念分層 19
2.7　關聯規則 20
第三章　研究方法 24
3.1　關聯規則探勘與集合 24
3.2　關聯規則與集合 26
3.3　自訂關聯規則探勘對象 32
3.4　自訂群組對象 34
3.5　群組計數 35
3.6　實體叢集集合圖示定義 37
3.7 演算法 40
第四章　實驗與論證 42
4.1　實驗一原始一維關聯規則探勘 43
4.2　實驗二原始一維關聯規則探勘方法+群組計數 54
4.3　實驗三使用自訂群組、自訂觀察、群組對象、群組計數方法實作一維關聯規則探勘 59
第五章　結論 70

[1] Agrawal, R., Imielinksi, T., Swami, A., "Mining association rules between sets of items in large database", The ACM SIGMOD Conference, Washington DC, USA, 1993.
[2] Agrawal, R., Srikant, R., "Fast algorithm for mining association rules", The International Conference on Very Large Data Bases, 3th, pp. 487-499, 1994.
[3] Agrawal, R., Srikant, R., Vu, Q., "Mining association rules with item constraints", In 3^thInternational Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, California, 1997.
[4] Agrawal, R., Srikant, R., "Mining generalized association rules", The 〖21〗^thInternational Conference on Very Large Data Bases, Zurich, Swizerland, pp. 407-419, 1995.
[5] Agrawal, R., Srikant, R., "Mining quantitative association rules in large relational tables", The 1996 ACM SIGMOD International Conference on Management of Data, Monreal, Canada, June, pp. 1-12, 1996.
[6] Agrawal, R., Srikant, R., " Mining Sequential Patterns: Generalizations and Performance Improvements", EDBT 1996, pp:3-17.
[7] Bing Liu, "Web Data Mining", 2009.
[8] Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y., "Maintenance of discovered association rules in large databases: An incremental updating approach", In 12th,〖12〗^th IEEE International Conference on Data Engineering, 1996.
[9] Gordon Linoff, S., Michael Berry, J.A., "Mining the Web", 2004.
[10] Mannila, H., Toivonen, H., Inkeri Verkamo, A.," Efficient algorithm for discovering association rules", Proceeding AAAI Workshop Knowledge Discovery in Databases, pp. 181-192, 1994.
[11] Jeffrey Hoffer, A., Mary Prescott, B., Fred McFadden, R., "Modern Database Management", 2002.
[12] Han, J., Fu, Y., "Discovery of multiple-level association rules from large database", In International Conference on Very Large Data Bases, Zurich, Swizerland, pp. 420-431, 1995.
[13] Han, J., Pei, J., Yin, Y., "Mining frequent patterns without candidate generation", In Proc. Of the ACM SIGMOD Conference on Management of data, 2000.
[14] Han, J., Micheline Kamber, "Data Mining: Concepts and Techniques", 2006.
[15] Park, J.S., Chen, M.S., Yu, P.S., "Using a hash-based method with transaction trimming for mining association rules", IEEE Transactions on Knowledge and Data Engineering, 21th, Vol. 9, No. 5, pp.812-825, 1997.
[16] Michael Berry, J.A., Gordon Linoff, S., "Data Mining Techniques:for marketing , sales,and customer support", 1997.
[17] Seymour Lipschutz, Marc Lipson, "Schaum's Outline of Discrete Mathematics", 6th, Second edition by 1997.
[18] Fukuda, T. , Morimoto, Y., Morishita, S., Tokuyama, T., "Mining optimized association rules for numeric attributes", The ACM IGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 21th, pp. 182-191, 1996.
[19] 曾憲雄, 蔡秀滿, 蘇東興, 曾秋蓉, 王慶堯(2007)，資料探勘 Data Mining，旗標出版股份有限公司。
[20] 丁賢一, 陳牧言(2005)，資料探勘 Data Mining，台中市：滄海書局。
[21] Michael Berry, J.A., Gordon Linoff (彭文正譯)1997，Data Mining 資料採礦-顧客關係管理暨電子行銷之應用，數博網資訊股份有限公司。