A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method
Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patter...
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| Published in | Journal of the American Society for Information Science and Technology Vol. 54; no. 7; pp. 660 - 670 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Wiley Subscription Services, Inc., A Wiley Company
01.05.2003
Wiley Periodicals Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1532-2882 2330-1635 1532-2890 2330-1643 |
| DOI | 10.1002/asi.10258 |
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| Abstract | Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Moreover, each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. In this method, each attribute is partitioned by its various fuzzy sets with pre‐specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily obtained. The main aim of this paper is exactly to propose a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method. |
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| AbstractList | Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Moreover, each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. In this method, each attribute is partitioned by its various fuzzy sets with pre‐specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily obtained. The main aim of this paper is exactly to propose a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method. Contribution to a special topic section on Web retrieval and mining: a machine learning perspective. Sequential patterns refer to the frequently occurring patterns related to time or other sequences and have been widely applied to solving decision problems. Since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. Each attribute is partitioned by its various fuzzy sets with pre-specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily obtained. Proposes a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method. (Original abstract - amended) Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Moreover, each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. In this method, each attribute is partitioned by its various fuzzy sets with pre-specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily obtained. The main aim of this paper is exactly to propose a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method. [PUBLICATION ABSTRACT] |
| Author | Chen, Ruey-Shun Hu, Yi-Chung |
| Author_xml | – sequence: 1 givenname: Ruey-Shun surname: Chen fullname: Chen, Ruey-Shun email: rschen@bis03.iim.nctu.edu.tw organization: Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan, ROC – sequence: 2 givenname: Yi-Chung surname: Hu fullname: Hu, Yi-Chung organization: Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan, ROC |
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| Notes | Nomenclature K, number of partitions in each quantitative attribute; k, length of a fuzzy sequence; d, degree of a given relation, where d ≥ 1; A K,i mx m, im-th linguistic value of K fuzzy partitions defined in quantitative attribute xm, 1 ≤ im ≤ K; μ K,i mx m, membership function of A K,i mx m; n, total number of customers; cr, r-th customer, where 1 ≤ r ≤ n; αr, number of consecutive transactions ordered by transaction-time for cr; β, total number of frequent fuzzy grids; t p(r), p-th transaction corresponding to cr, where t p(r) = (t p1(r), t p2(r), ..., t pd(r)), and 1 ≤ p ≤ αr; Lj, j-th frequent fuzzy grid, where 1 ≤ j≤ β. ark:/67375/WNG-D880PQPQ-B istex:FBDD5AF6FFB82D76A74CD9B6B86BAF80793CE908 ArticleID:ASI10258 A p1 fuzzy partitions defined in quantitative attribute number of consecutive transactions ordered by transaction‐time for p2 th frequent fuzzy grid, where 1 K L membership function of where th linguistic value of β, total number of frequent fuzzy grids and 1 c d im ≤ Nomenclature ( degree of a given relation, where i ) j , m th transaction corresponding to n p 1 α r t pd number of partitions in each quantitative attribute x length of a fuzzy sequence ; total number of customers th customer, where 1 μ β. = ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| References | Hu, Y.C., Chen, R.S., & Tzeng, G.H. Generating learning sequences for decision makers through data mining and competence set expansion. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 32(5), 679-686. Bezdek J.C. (1981). Pattern recognition with fuzzy objective function algorithms, New York: Plenum. Zimmermann, H.-J. (1996). Fuzzy set theory and its applications. Boston: Kluwer. Wang, L.X., & Mendel, J.M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6), 1414-1427. Han J.W., & Kamber M. (2001). Data mining: concepts and techniques. San Francisco: Morgan Kaufmann. 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. Myra, S. (2000). Web usage mining for web site evaluation. Communications of the ACM, 43(8), 127-134. Berry, M., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support. New York: John Wiley & Sons. 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. Zadeh, L.A. (1975, 1976) The concept of a linguistic variable and its application to approximate reasoning, Information Science, 8(3), 199-249(part 1); 8(4), 301-357(part 2); 9(1), 43-80(part 3). Jang, J.S.R. (1993). ANFIS: adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. Zimmermann, H.-J. (1991). Fuzzy sets, decision making, and expert systems. Boston: Kluwer. Pedrycz, W. (1994). Why triangular membership functions? Fuzzy Sets and Systems 64, 21-30. Hong T.P., Wang T.T., Wang, S.L., & Chien, B.C. (2000). Learning a coverage set of maximally general fuzzy rules by rough sets. Expert Systems with Applications, 19(2), 97-103. Pedrycz, W., & Gomide, F. (1998). An Introduction to Fuzzy Sets: Analysis and Design, Cambridge: MIT Press. 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. Ravi, V., Zimmermann, H.-J. (2000). Fuzzy rule based classification with FeatureSelector and modified threshold accepting. European Journal of Operational Research, 123(1), 16-28. Chen, S.M., & Jong, W.T. (1997). Fuzzy query translation for relational database systems. IEEE Transactions on Systems, Man, and Cybernetics, 27(4), 714-721. Homaifar, A., & McCormick, E. (1995). Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Transactions on Fuzzy Systems, 3(2), 129-139. Ishibuchi, H., Nakashima T., & Murata, T. (2001). Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences, 136, 109-133. Jang, J.S.R., & Sun, C.T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378-406. Saaty, T.L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. New York: McGraw-Hill. Sharma, S. (1996). Applied multivariate techniques. Singapore: John Wiley & Sons. Zadeh, L.A. (1965). Fuzzy sets. Information Control, 8(3), 338-353. Ishibuchi H., & Nii, M. (2001). Numerical analysis of the learning of fuzzified neural networks from fuzzy if-then rules. Fuzzy Sets and Systems 120, 281-307. Hu Y.C., Tzeng G.H., & Chen R. S. (2002). Mining fuzzy association rules for classification problems. Computers and Industrial Engineering, 43(4), 735-750. 1993; 23 2001; 120 1999; 29 2000; 43 1998 1997 1996 1995 1997; 27 1993 1991 1995; 3 32 1994; 64 1995; 83 2000; 19 1975, 1976; 8 2001 1965; 8 2002; 43 1981 2000; 123 1980 1992; 22 1994; 2 2001; 136 e_1_2_7_6_1 e_1_2_7_4_1 e_1_2_7_9_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 Zimmermann H.‐J. (e_1_2_7_29_1) 1991 Agrawal R. (e_1_2_7_3_1) 1996 Berry M. (e_1_2_7_5_1) 1997 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_27_1 Hu Y.C. (e_1_2_7_11_1); 32 e_1_2_7_28_1 Sharma S. (e_1_2_7_25_1) 1996 e_1_2_7_30_1 e_1_2_7_31_1 e_1_2_7_23_1 e_1_2_7_22_1 e_1_2_7_21_1 e_1_2_7_20_1 Han J.W. (e_1_2_7_8_1) 2001 Saaty T.L. (e_1_2_7_24_1) 1980 |
| References_xml | – reference: Ravi, V., Zimmermann, H.-J. (2000). Fuzzy rule based classification with FeatureSelector and modified threshold accepting. European Journal of Operational Research, 123(1), 16-28. – reference: Pedrycz, W., & Gomide, F. (1998). An Introduction to Fuzzy Sets: Analysis and Design, Cambridge: MIT Press. – reference: Jang, J.S.R., & Sun, C.T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378-406. – reference: Myra, S. (2000). Web usage mining for web site evaluation. Communications of the ACM, 43(8), 127-134. – reference: Hu Y.C., Tzeng G.H., & Chen R. S. (2002). Mining fuzzy association rules for classification problems. Computers and Industrial Engineering, 43(4), 735-750. – reference: Zimmermann, H.-J. (1996). Fuzzy set theory and its applications. Boston: Kluwer. – reference: Homaifar, A., & McCormick, E. (1995). Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Transactions on Fuzzy Systems, 3(2), 129-139. – reference: Saaty, T.L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. New York: McGraw-Hill. – reference: Hong T.P., Wang T.T., Wang, S.L., & Chien, B.C. (2000). Learning a coverage set of maximally general fuzzy rules by rough sets. Expert Systems with Applications, 19(2), 97-103. – reference: Ishibuchi, H., Nakashima T., & Murata, T. (2001). Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences, 136, 109-133. – reference: Berry, M., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support. New York: John Wiley & Sons. – reference: Wang, L.X., & Mendel, J.M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6), 1414-1427. – reference: Bezdek J.C. (1981). Pattern recognition with fuzzy objective function algorithms, New York: Plenum. – reference: Zadeh, L.A. (1975, 1976) The concept of a linguistic variable and its application to approximate reasoning, Information Science, 8(3), 199-249(part 1); 8(4), 301-357(part 2); 9(1), 43-80(part 3). – reference: Hu, Y.C., Chen, R.S., & Tzeng, G.H. Generating learning sequences for decision makers through data mining and competence set expansion. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 32(5), 679-686. – reference: Pedrycz, W. (1994). Why triangular membership functions? Fuzzy Sets and Systems 64, 21-30. – reference: Sharma, S. (1996). Applied multivariate techniques. Singapore: John Wiley & Sons. – reference: Chen, S.M., & Jong, W.T. (1997). Fuzzy query translation for relational database systems. IEEE Transactions on Systems, Man, and Cybernetics, 27(4), 714-721. – reference: 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. – reference: Zimmermann, H.-J. (1991). Fuzzy sets, decision making, and expert systems. Boston: Kluwer. – reference: Ishibuchi H., & Nii, M. (2001). Numerical analysis of the learning of fuzzified neural networks from fuzzy if-then rules. Fuzzy Sets and Systems 120, 281-307. – reference: 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. – reference: Han J.W., & Kamber M. (2001). Data mining: concepts and techniques. San Francisco: Morgan Kaufmann. – reference: Jang, J.S.R. (1993). ANFIS: adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. – reference: 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. – reference: Zadeh, L.A. (1965). Fuzzy sets. 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| Snippet | Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems.... Contribution to a special topic section on Web retrieval and mining: a machine learning perspective. Sequential patterns refer to the frequently occurring... |
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| SubjectTerms | Data Analysis Data mining Fuzzy logic Fuzzy set theory Fuzzy sets Fuzzy systems Information retrieval Information science Intelligibility Knowledge bases (artificial intelligence) Knowledge representation Natural language Online information retrieval Partition method Partitions Prototypes Research subjects Searching Studies |
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| Title | A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method |
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