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題名：使用模糊分割自概念層級架構中找出關聯規則
書刊名：資訊管理學報
作者：胡宜中 引用關係
作者(外文)：Hu, Yi-chung
出版日期：2006
卷期：13:3
頁次：頁63-80
主題關鍵詞：模糊集合；關聯規則；資料探勘；概念層級架構；決策支援；Fuzzy sets；Association rules；Data mining；Concept hierarchy；Decision support
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關聯規則可輔助管理者進行行銷策略與商場架位之規畫，然而大部份的探勘方法均僅針對實體項目或商品進行分析。有別於僅在單一層紋中找出關聯規則，以及考量由使用者感認與主觀判斷所產生的認知不確定性，本研究的目的在於使用模糊分割，以提出一個可自概念層級架構中找出模糊關聯規則的方法。所提出之方法主要是由兩階段所組成：在第一階段中依據層級架構將資料項目做適當的抽象化，並找出高頻的模糊格；而在第二階段中係由高頻模糊格進一步產生多層級模糊關聯規則。其特色在於使用表格結構以存放高頻模糊格，且高頻模糊格與多層級模糊關聯規則均為使用布林運算所產生。本文亦探討所提出方法在不同的資料庫大小與相關參數設定下，對執行時間與關聯規則之產生所造成的影響。實驗結果顯示所提出方法可有效提升執行效率。
Association rules can help managers to plan marketing or design store layouts. However, many methods are developed by analyzing the relationships among data items at a single level. Since cognitive uncertainty arising from human perception, cognition and subject judgment should be taken into account, in this paper, a new method is proposed to mine multiple-level fuzzy association rules among data items at a concept hierarchy, using fuzzy partition by a simple fuzzy grid. The proposed method primarily consists of two phases: one to find frequent fuzzy grids at each level, and the other to generate multiple-level fuzzy association rules from those frequent patterns. The main feature of the proposed method is to employ the table structure to store frequent fuzzy grids. In particular, both frequent fuzzy grids and multiple-level fuzzy association rules can be efficiently generated by applying the Boolean operations on the table structure. To understand the impact of the proposed method on the execution time and the number of generated association rules, the experiments are performed by using different sizes of databases and thresholds. The experimental results demonstrate the proposed method is efficient.
期刊論文
1.Yuan, Y.、Shaw, M. J.(1995)。Induction of Fuzzy Decision Trees。Fuzzy Sets and Systems，69，125-139。  new window
2.Zadeh, L. A.(1975)。The concept of a linguistic variable and its application to approximate reasoning。Information Science，8，43-357。  new window
3.Agrawal, R.、Shafer, J. C.(1996)。Parallel Mining of Association Rules: Design, Implementation, and Experience。IEEE Transactions on Knowledge and Data Engineering，8(6)，962-969。  new window
4.Zadeh, L. A.(1975)。The concept of a linguistic variable and its application to approximate reasoning。Information Sciences，9(1)，43-80。  new window
5.Zadeh, L. A.(1975)。The Concept of a Linguistic Variable and its Application to Approximate Reasoning。Information Science，8(3)，199-249。  new window
6.Ishibuchi, H.、Nozaki, K.、Yamamoto, N.、Tanaka, H.(1995)。Selecting fuzzy if then rules for classification problems using genetic algorithms。IEEE Transactions on Fuzzy Systems，3(3)，260-270。  new window
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9.Pedrycz, W.(1994)。Why Triangular Membership Functions?。Fuzzy Sets and Systems，64，21-30。  new window
10.Hong, T. P.、Lin, K. Y.、Chien, B. C.(2003)。Mining Fuzzy Multiple-Level Association Rules from Quantitative Data。Applied Intelligence，18(1)，79-90。  new window
11.Hu, Yi-Chung、Chen, Ruey-Shun、曾國雄(2003)。Discovering fuzzy association rules using fuzzy partition methods。Journal of Knowledge-Based Systems，16，137-147。  new window
12.Bradley, P. S.、Gehrke, J.、Ramakrishnan, R.、Srikant, R.(2002)。Scaling Mining Algorithms to Large Databases。Communications of the ACM，45(8)，38-43。  new window
13.Han, E.-H.、Karypis, G.、Kumar, V.(2000)。Scalable Parallel Data Mining for Association Rules。IEEE Transactions on Knowledge and Data Engineering，12(3)，337-352。  new window
14.Hong, T. P.、Lin, K. Y.、Wang, S. L.(2003)。Fuzzy Data Mining for Interesting Generalized Association Rules。Fuzzy Sets and Systems，138，255-269。  new window
15.Hu, Y. C.、Chen, R. S.、Tzeng, G. H.(2003)。Finding Fuzzy Classification Rules Using Data Mining Techniques。Pattern Recognition Letters，24，509-519。  new window
16.Ishibuchi, H.、Nakashima, T.、Murata, T.(1999)。Performance Evaluation of Fuzzy Classifier Systems for Multidimensional Pattern Classification Problems。IEEE Transactions on Systems, Man, and Cybernetics，29(5)，601-618。  new window
17.Liu, B.、Ma, Y.、Wong, C. K.、Yu, P. S.(2003)。Scoring the Data Using Association Rules。Applied Intelligence，18(2)，119-135。  new window
18.Sun, C. T.(1994)。Rule-base Structure Identification in an Adaptive-network-based Fuzzy Inference System。IEEE Transactions on Fuzzy Systems，2(1)，64-73。  new window
19.Wand, L. X.、Mendel, J. M.(1992)。Generating Fuzzy Rules by Learning form Examples。IEEE Transactions on Systems, Man, and Cybernetics，22(6)，1414-1427。  new window
會議論文
1.Liu B.、Hsu W.、Ma T.(1999)。Mining association rules with multiple minimum supports。ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-99)。San Diego, USA。337-341。  new window
2.Han, J.、Fu, Y.(1995)。Discovery of Multiple-level Association Rules from Large Databases。Zürich, Switzerland。420-431。  new window
3.Srikant, R.、Agrawal, R.(1996)。Mining Quantitative Association Rules in Large Relational Tables。The ACM-SIGMOD International Conferences。Montreal：ACM Digital Library。1-12。  new window
4.Agrawal, Rakesh、Gehrke, J.、Gunopulos, D.、Raghavan, P.(1998)。Automatic subspace clustering of high dimensional data for data mining applications。The 1998 ACM SIGMOD international conference on Management of data，94-105。  new window
5.Wur, S. Y.、Leu, Yungho(1999)。An Effective Boolean Algorithm for Mining Association Rules in Large Databases。6th International Conference on Database Systems for Advanced Applications。IEEE。179-186。  new window
6.Fukuda, T.、Morimoto, Y.、Morishita, S.、Tokuyama, T.(1996)。Mining Optimized Association Rules for Numeric Attributes。0。182-191。  new window
7.Ishibuchi, H.、Nakashima, T.、Yamamoto, T.(2001)。Fuzzy Association Rules for Handing Continuous Attributes。0。118-121。  new window
8.Liu, B.、Hsu, W.、Ma, Y.(1998)。Integrating Classification and Association Rule Mining。New York, NY。80-86。  new window
9.Rastogi, R.、Shim, K.(1999)。Mining Optimized Support Rules for Numeric Attributes。Sydney, Australia。206-215。  new window
圖書
1.Bezdeck, J. C.(1981)。Pattern Recognition with Fuzzy Objective Function Algorithms。Kluwer Academic Publishers。  new window
2.Jang, Jyh-Shing Roger、Sun, Chuen-Tsai、Mizutani, Eiji(1997)。Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence。Upper Saddle River, NJ：Prentice Hall。  new window
3.Kosko, B. J.(1992)。Neural Networks and Fuzzy Systems: A Dynamic Systems Approach to Machine Intelligence。Englewood Cliffs, New Jersey：Prentice-Hill。  new window
4.Han, Jiawei、Kamber, Micheline(2001)。Data mining: concepts and techniques。Oxford, CA：Morgan Kaufmann Publisher, Inc.。  new window
5.Berry, Michael J. A.、Linoff, Gordon S.(1997)。Data Mining Techniques for Marketing, Sales and Customer Support。John Wiley & Sons, Inc.。  new window
6.Agrawal, R.、Mannila, H.、Srikant, R.、Toivonen, H.、Verkamo, A. I.、Agrawal, Rakesh、Mannila, Heikki、Srikant, Ramakrishnan、Toivonen, Hannu、Verkamo, A. Inkeri(1996)。Fast Discovery of Association Rules。Advances in Knowledge Discovery and Data Mining。Menlo Park, CA。  new window
7.Pedrycz, W.、Gomide, F.(1998)。An Introduction to Fuzzy Set: Analysis and Design。An Introduction to Fuzzy Set: Analysis and Design。Cambridge, MA。  new window
 
 
 
 
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