Multi-level fuzzy mining with multiple minimum supports
Finding association rules in transaction databases is most commonly seen in data mining. In real applications, different items may have different support criteria to judge their importance, taxonomic relationships among items may appear, and data may have quantitative values. This paper thus propose...
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| Published in | Expert systems with applications Vol. 34; no. 1; pp. 459 - 468 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2006.09.011 |
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| Abstract | Finding association rules in transaction databases is most commonly seen in data mining. In real applications, different items may have different support criteria to judge their importance, taxonomic relationships among items may appear, and data may have quantitative values. This paper thus proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in quantitative transactions with multiple minimum supports of items. Items may have different minimum supports and the maximum-itemset minimum-taxonomy support constraint is adopted to discover the large itemsets. Under the constraint, the characteristic of downward-closure is kept, such that the original apriori algorithm can be easily extended to find fuzzy large itemsets. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. It can also discover cross-level fuzzy association rules under the maximum-itemset minimum-taxonomy support constraint. An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level association rules under multiple item supports in a simple and effective way. |
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| AbstractList | Finding association rules in transaction databases is most commonly seen in data mining. In real applications, different items may have different support criteria to judge their importance, taxonomic relationships among items may appear, and data may have quantitative values. This paper thus proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in quantitative transactions with multiple minimum supports of items. Items may have different minimum supports and the maximum-itemset minimum-taxonomy support constraint is adopted to discover the large itemsets. Under the constraint, the characteristic of downward-closure is kept, such that the original apriori algorithm can be easily extended to find fuzzy large itemsets. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. It can also discover cross-level fuzzy association rules under the maximum-itemset minimum-taxonomy support constraint. An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level association rules under multiple item supports in a simple and effective way. |
| Author | Wang, Tien-Chin Hong, Tzung-Pei Lee, Yeong-Chyi |
| Author_xml | – sequence: 1 givenname: Yeong-Chyi surname: Lee fullname: Lee, Yeong-Chyi email: d9003007@stmail.isu.edu.tw organization: Department of Information Engineering, I-Shou University, Kaohsiung 84008, Taiwan, ROC – sequence: 2 givenname: Tzung-Pei surname: Hong fullname: Hong, Tzung-Pei email: tphong@nuk.edu.tw organization: Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, ROC – sequence: 3 givenname: Tien-Chin surname: Wang fullname: Wang, Tien-Chin email: tcwang@isu.edu.tw organization: Department of Information Management, I-Shou University, Kaohsiung 84008, Taiwan, ROC |
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| Cites_doi | 10.1002/int.4550100402 10.1016/S0165-0114(97)00181-4 10.1016/j.ijar.2004.11.006 10.1016/0165-0114(95)00305-3 10.1016/0165-0114(94)00229-Z 10.1016/0165-0114(93)90470-3 10.1016/S0165-0114(98)00179-1 10.1109/69.250074 10.1109/69.553155 10.1145/223784.223813 10.1145/237661.237708 10.1109/ICDE.1999.754926 10.1145/233269.233311 10.1145/170036.170072 10.1016/0165-0114(93)90125-2 10.1016/S1088-467X(99)00028-1 10.1016/S1088-467X(98)00007-9 10.1145/312129.312274 10.1109/FUZZY.1996.551712 |
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