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https://hdl.handle.net/2440/80824
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DC Field | Value | Language |
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dc.contributor.author | Liu, Q. | - |
dc.contributor.author | Shi, P. | - |
dc.contributor.author | Hu, Z. | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | ICIC Express Letters, Part B: Applications, 2013; 4(1):121-128 | - |
dc.identifier.issn | 2185-2766 | - |
dc.identifier.uri | http://hdl.handle.net/2440/80824 | - |
dc.description.abstract | Efficient mining of Strong Jumping Emerging Patterns (SJEPs) is useful for constructing accurate classifiers. The method for mining SJEPs based on a contrast pattern tree structure (CP-Tree) has been demonstrated to perform extremely well for a low-dimensional dataset. In the method, a large number of non-minimal JEPs are generated during the mining process. So, it is unable to handle higher-dimensional attributes. In this paper, we propose a novel pattern pruning technique that dramatically reduces the search space. The CP-tree method is greatly improved by the proposed pattern pruning technique. Experiments are performed on two high-dimensional cancer datasets. Compared with the original CP-tree algorithm, the results show that the improved CP-tree algorithm is substantially faster, and able to handle higher-dimensional attributes. | - |
dc.description.statementofresponsibility | Quanzhong Liu, Peng Shi and Zhengguo Hu | - |
dc.language.iso | en | - |
dc.publisher | ICIC International | - |
dc.rights | Copyright status unknown | - |
dc.source.uri | http://www.ijicic.org/elb-4(1).htm | - |
dc.subject | CP-tree | - |
dc.subject | Data mining | - |
dc.subject | Pattern pruning | - |
dc.subject | SJEPs | - |
dc.title | Fast algorithms for mining Strong Jumping Emerging Patterns using the contrast pattern tree | - |
dc.type | Journal article | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Shi, P. [0000-0001-8218-586X] | - |
Appears in Collections: | Aurora harvest 4 Electrical and Electronic Engineering publications |
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