| Pushing Multiple Convertible Constrains into Frequent Itemsets Mining |
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| 引用本文： | SONG Baoli QIN Zheng. Pushing Multiple Convertible Constrains into Frequent Itemsets Mining[J]. 武汉大学学报:自然科学英文版, 2006, 11(5): 1120-1125. DOI: 10.1007/BF02829221 |
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| 作者姓名： | SONG Baoli QIN Zheng |
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| 作者单位： | [1]School of Computer Science and Technology, Xi＇anJiaotong University, Xi＇an 710049, Shaanxi, China [2]Shenzhen Labor and Social Security Bureau, Shenzhen518029, Guangdong, China |
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| 摘 要： | Constraint pushing techniques have been developed for mining frequent patterns and association rules. How ever, multiple constraints cannot be handled with existing techniques in frequent pattern mining. In this paper, a new algorithm MCFMC （mining complete set of frequent itemsets with multiple constraints） is introduced. The algorithm takes advantage of the fact that a convertible constraint can be pushed into mining algorithm to reduce mining research spaces. By using a sample database, the algorithm develops techniques which select an optimal method based on a sample database to convert multiple constraints into multiple convert ible constraints, disjoined by conjunction and/or, and then partition these constraints into two parts. One part is pushed deep inside the mining process to reduce the research spaces for frequent itemsets, the other part that cannot be pushed in algorithm is used to filter the complete set of frequent itemsets and get the final result. Results from our detailed experi ment show the feasibility and effectiveness of the algorithm.
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| 关 键 词： | 可逆约束 数据挖掘 多重约束 数据库 |
| 文章编号： | 1007-1202（2006）05-1120-06 |
| 收稿时间： | 2006-02-10 |
Pushing multiple convertible constrains into frequent itemsets mining |
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| Song Baoli,Qin Zheng. Pushing multiple convertible constrains into frequent itemsets mining[J]. Wuhan University Journal of Natural Sciences, 2006, 11(5): 1120-1125. DOI: 10.1007/BF02829221 |
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| Authors: | Song Baoli Qin Zheng |
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| Affiliation: | (1) School of Computer Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China;(2) Shenzhen Labor and Social Security Bureau, 518029 Shenzhen, Guangdong, China |
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| Abstract: | Constraint pushing techniques have been developed for mining frequent patterns and association rules. However, multiple constraints cannot be handled with existing techniques in frequent pattern mining. In this paper, a new algorithm MCFMC (mining complete set of frequent itemsets with multiple constraints) is introduced. The algorithm takes advantage of the fact that a convertible constraint can be pushed into mining algorithm to reduce mining research spaces. By using a sample database, the algorithm develops techniques which select an optimal method based on a sample database to convert multiple constraints into multiple convertible constraints, disjoined by conjunction and/or, and then partition these constraints into two parts. One part is pushed deep inside the mining process to reduce the research spaces for frequent itemsets, the other part that cannot be pushed in algorithm is used to filter the complete set of frequent itemsets and get the final result. Results from our detailed experiment show the feasibility and effectiveness of the algorithm. |
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| Keywords: | convertible constraints data mining multiple constraints sample database |
| 本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录！ |
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