A data mining algorithm for fuzzy transaction data
The main purpose of this paper is to propose a data mining algorithm for finding interesting association rules from given sets of fuzzy transaction data. To efficiently resolve the ambiguity frequently arising in available information and do more justice to the essential fuzziness in human judgment...
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| Published in | Quality & quantity Vol. 48; no. 6; pp. 2963 - 2971 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.11.2014
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0033-5177 1573-7845 |
| DOI | 10.1007/s11135-013-9934-1 |
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| Abstract | The main purpose of this paper is to propose a data mining algorithm for finding interesting association rules from given sets of fuzzy transaction data. To efficiently resolve the ambiguity frequently arising in available information and do more justice to the essential fuzziness in human judgment and preference, the trapezoidal fuzzy numbers are used to describe the fuzzy assessments of transaction data. Then, combining the concepts of fuzzy set theory and the priori algorithms, the interesting item sets are found to construct the association rules. Finally, a numerical example is used to demonstrate the computational process of proposed data mining algorithm. By utilizing this data mining algorithm, the decision-makers’ fuzzy assessments with various rating attitudes can be taken into account in the data mining process to assure more convincing and accurate knowledge discovery. |
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| AbstractList | The main purpose of this paper is to propose a data mining algorithm for finding interesting association rules from given sets of fuzzy transaction data. To efficiently resolve the ambiguity frequently arising in available information and do more justice to the essential fuzziness in human judgment and preference, the trapezoidal fuzzy numbers are used to describe the fuzzy assessments of transaction data. Then, combining the concepts of fuzzy set theory and the priori algorithms, the interesting item sets are found to construct the association rules. Finally, a numerical example is used to demonstrate the computational process of proposed data mining algorithm. By utilizing this data mining algorithm, the decision-makers' fuzzy assessments with various rating attitudes can be taken into account in the data mining process to assure more convincing and accurate knowledge discovery.[PUBLICATION ABSTRACT] The main purpose of this paper is to propose a data mining algorithm for finding interesting association rules from given sets of fuzzy transaction data. To efficiently resolve the ambiguity frequently arising in available information and do more justice to the essential fuzziness in human judgment and preference, the trapezoidal fuzzy numbers are used to describe the fuzzy assessments of transaction data. Then, combining the concepts of fuzzy set theory and the priori algorithms, the interesting item sets are found to construct the association rules. Finally, a numerical example is used to demonstrate the computational process of proposed data mining algorithm. By utilizing this data mining algorithm, the decision-makers’ fuzzy assessments with various rating attitudes can be taken into account in the data mining process to assure more convincing and accurate knowledge discovery. The main purpose of this paper is to propose a data mining algorithm for finding interesting association rules from given sets of fuzzy transaction data. To efficiently resolve the ambiguity frequently arising in available information and do more justice to the essential fuzziness in human judgment and preference, the trapezoidal fuzzy numbers are used to describe the fuzzy assessments of transaction data. Then, combining the concepts of fuzzy set theory and the priori algorithms, the interesting item sets are found to construct the association rules. Finally, a numerical example is used to demonstrate the computational process of proposed data mining algorithm. By utilizing this data mining algorithm, the decision-makers' fuzzy assessments with various rating attitudes can be taken into account in the data mining process to assure more convincing and accurate knowledge discovery. Reprinted by permission of Springer |
| Audience | Academic |
| Author | Su, Yuhling Liao, Mao-Sheng Liang, Gin-Shuh Chen, Chin-Yuan |
| Author_xml | – sequence: 1 givenname: Chin-Yuan surname: Chen fullname: Chen, Chin-Yuan organization: Department of Shipping and Transportation Management, National Taiwan Ocean University – sequence: 2 givenname: Gin-Shuh surname: Liang fullname: Liang, Gin-Shuh email: gsliang@mail.ntou.edu.tw organization: Department of Shipping and Transportation Management, National Taiwan Ocean University – sequence: 3 givenname: Yuhling surname: Su fullname: Su, Yuhling organization: Department of Shipping and Transportation Management, National Taiwan Ocean University – sequence: 4 givenname: Mao-Sheng surname: Liao fullname: Liao, Mao-Sheng organization: Department of Shipping and Transportation Management, National Taiwan Ocean University |
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| CitedBy_id | crossref_primary_10_1007_s11135_022_01480_z crossref_primary_10_1108_JBIM_09_2018_0269 |
| Cites_doi | 10.1080/00207727808941724 10.1016/0888-613X(87)90015-6 10.1016/j.mcm.2010.07.022 10.1016/S0167-9236(03)00090-3 10.1016/S0167-9236(03)00008-3 10.1016/0020-0255(75)90017-1 10.1016/j.patrec.2005.11.020 10.1016/S0020-0255(99)00050-X 10.1016/j.fss.2005.05.036 10.1016/S0165-0114(97)00146-2 10.1016/j.eswa.2009.02.028 10.1016/j.ins.2003.03.014 10.1016/S0019-9958(65)90241-X 10.1016/S0165-0114(98)00294-2 10.1016/0020-0255(75)90036-5 10.1016/j.fss.2003.10.006 10.1080/00137919508903160 10.1080/01969720302840 10.1016/S1088-467X(99)00028-1 10.1016/0165-0114(92)90088-L 10.1016/0165-0114(95)00185-9 10.1016/j.ins.2010.10.029 10.1016/j.engappai.2003.09.007 10.1016/S0167-9236(02)00098-2 |
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| Keywords | Fuzzy similarity Trapezoidal fuzzy numbers Data mining Association rule |
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| SubjectTerms | Algorithms Ambiguity Consumption Data Data mining Decision makers Decision making Evaluation Expenditures Fuzzy sets Judgement Knowledge Knowledge management Linguistics Methodology of the Social Sciences Set theory Social Sciences Statistical analysis |
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| Title | A data mining algorithm for fuzzy transaction data |
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