Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining
Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorith...
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| Vydáno v: | Journal of intelligent information systems Ročník 31; číslo 3; s. 243 - 264 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Boston
Springer US
01.12.2008
Springer Nature B.V |
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| ISSN: | 0925-9902, 1573-7675 |
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| Abstract | Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules. |
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| AbstractList | Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules. Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.'s work in terms of runtime, number of large itemsets and number of association rules. Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.'s work in terms of runtime, number of large itemsets and number of association rules. [PUBLICATION ABSTRACT] |
| Author | Alhajj, Reda Kaya, Mehmet |
| Author_xml | – sequence: 1 givenname: Reda surname: Alhajj fullname: Alhajj, Reda email: alhajj@cpsc.ucalgary.ca organization: Department of Computer Science, University of Calgary, Department of Computer Science, Global University – sequence: 2 givenname: Mehmet surname: Kaya fullname: Kaya, Mehmet organization: Department of Computer Engineering, Firat University |
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| Cites_doi | 10.1016/S0019-9958(65)90241-X 10.1016/S0306-4379(01)00008-4 10.1109/4235.797969 10.1016/S0165-0114(96)00377-6 10.1023/A:1006504901164 10.1145/273244.273257 10.1016/S1088-467X(99)00028-1 10.1016/S0165-0114(99)00065-2 10.1007/3-540-46146-9_14 10.7551/mitpress/1090.001.0001 10.1109/ISIE.2001.931767 10.1109/FUZZY.1996.552395 10.1007/978-3-662-02830-8 10.1145/253260.253361 10.1109/CAIA.1995.378813 10.1145/233269.233311 10.1109/ICDE.1997.581756 10.1145/266714.266898 10.1109/TAI.1999.809772 |
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| Keywords | Multi-objective genetic algorithms CURE Fuzziness Automated clustering Data mining Fuzzy association rules |
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| References_xml | – ident: CR22 – volume: 8 start-page: 338 year: 1965 end-page: 353 ident: CR28 article-title: Fuzzy sets publication-title: Information and Control doi: 10.1016/S0019-9958(65)90241-X – volume: 26 start-page: 35 issue: 1 year: 2001 end-page: 58 ident: CR8 article-title: Cure: An efficient clustering algorithm for large databases publication-title: Information Systems doi: 10.1016/S0306-4379(01)00008-4 – ident: CR4 – ident: CR14 – ident: CR2 – ident: CR16 – volume: 3 start-page: 257 issue: 4 year: 1999 end-page: 271 ident: CR30 article-title: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.797969 – ident: CR6 – ident: CR29 – ident: CR25 – ident: CR27 – volume: 2 start-page: 1488 year: 1996 end-page: 1496 ident: CR11 article-title: Linguistic data mining and fuzzy modelling publication-title: Proceedings of IEEE International Conference on Fuzzy Systems – volume: 98 start-page: 279 year: 1998 end-page: 290 ident: CR23 article-title: Fuzzy sets technology in knowledge discovery publication-title: Fuzzy Sets and Systems doi: 10.1016/S0165-0114(96)00377-6 – ident: CR21 – ident: CR19 – volume: 12 start-page: 265 issue: 4 year: 1998 end-page: 319 ident: CR10 article-title: Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis publication-title: Artificial Intelligence Review doi: 10.1023/A:1006504901164 – ident: CR3 – ident: CR17 – ident: CR13 – ident: CR9 – year: 1992 ident: CR12 publication-title: Adaptation in natural and artificial systems – volume: 17 start-page: 41 issue: 1 year: 1998 end-page: 46 ident: CR18 article-title: Mining fuzzy association rules in databases publication-title: SIGMOD Record doi: 10.1145/273244.273257 – volume: 3 start-page: 363 year: 1999 end-page: 376 ident: CR15 article-title: Mining association rules from quantitative data publication-title: Intelligent Data Analysis doi: 10.1016/S1088-467X(99)00028-1 – volume: 118 start-page: 297 issue: 2 year: 2001 end-page: 306 ident: CR1 article-title: Determination of fuzzy logic membership functions using genetic algorithms publication-title: Fuzzy Sets and Systems doi: 10.1016/S0165-0114(99)00065-2 – ident: CR5 – year: 1989 ident: CR7 publication-title: Genetic algorithms in search, optimization, and machine learning – ident: CR26 – ident: CR24 – ident: CR20 – volume: 12 start-page: 265 issue: 4 year: 1998 ident: 44_CR10 publication-title: Artificial Intelligence Review doi: 10.1023/A:1006504901164 – volume: 8 start-page: 338 year: 1965 ident: 44_CR28 publication-title: Information and Control doi: 10.1016/S0019-9958(65)90241-X – ident: 44_CR25 – volume-title: Genetic algorithms in search, optimization, and machine learning year: 1989 ident: 44_CR7 – ident: 44_CR17 doi: 10.1007/3-540-46146-9_14 – volume: 98 start-page: 279 year: 1998 ident: 44_CR23 publication-title: Fuzzy Sets and Systems doi: 10.1016/S0165-0114(96)00377-6 – volume-title: Adaptation in natural and artificial systems year: 1992 ident: 44_CR12 doi: 10.7551/mitpress/1090.001.0001 – ident: 44_CR16 doi: 10.1109/ISIE.2001.931767 – volume: 2 start-page: 1488 year: 1996 ident: 44_CR11 publication-title: Proceedings of IEEE International Conference on Fuzzy Systems doi: 10.1109/FUZZY.1996.552395 – ident: 44_CR20 doi: 10.1007/978-3-662-02830-8 – volume: 3 start-page: 257 issue: 4 year: 1999 ident: 44_CR30 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.797969 – ident: 44_CR5 – volume: 118 start-page: 297 issue: 2 year: 2001 ident: 44_CR1 publication-title: Fuzzy Sets and Systems doi: 10.1016/S0165-0114(99)00065-2 – ident: 44_CR9 – volume: 26 start-page: 35 issue: 1 year: 2001 ident: 44_CR8 publication-title: Information Systems doi: 10.1016/S0306-4379(01)00008-4 – volume: 3 start-page: 363 year: 1999 ident: 44_CR15 publication-title: Intelligent Data Analysis – ident: 44_CR21 doi: 10.1145/253260.253361 – ident: 44_CR27 doi: 10.1109/CAIA.1995.378813 – ident: 44_CR14 – ident: 44_CR24 doi: 10.1145/233269.233311 – ident: 44_CR22 – ident: 44_CR19 doi: 10.1109/ICDE.1997.581756 – ident: 44_CR26 – volume: 17 start-page: 41 issue: 1 year: 1998 ident: 44_CR18 publication-title: SIGMOD Record doi: 10.1145/273244.273257 – ident: 44_CR2 – ident: 44_CR3 doi: 10.1145/266714.266898 – ident: 44_CR6 – ident: 44_CR13 – ident: 44_CR4 – ident: 44_CR29 doi: 10.1109/TAI.1999.809772 |
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| SubjectTerms | Artificial Intelligence Associations Automation Chromosomes Clustering Computer Science Data mining Data Structures and Information Theory Fuzzy logic Fuzzy sets Genetic algorithms Information Storage and Retrieval IT in Business Natural Language Processing (NLP) Optimization Set theory Studies |
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| Title | Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining |
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