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|dc.identifier.citation||International Journal of Systems Science, 2014; 45(3):598-615||en|
|dc.description.abstract||It is a great challenge to discover strong jumping emerging patterns (SJEPs) from a high-dimensional dataset because of the huge pattern space. In this article, we propose a dynamically growing contrast pattern tree (DGCP-tree) structure to store grown patterns and their path codes arrays with 1-bit counts, which are from the constructed bit string compression tree. A method of mining SJEPs based on DGCP-tree is developed. In order to reduce the pattern search space, we introduce a novel pattern pruning method, which dramatically reduces non-minimal jumping emerging patterns (JEPs) during the mining process. Experiments are performed on three real cancer datasets and three datasets from the University of California, Irvine machine-learning repository. Compared with the well-known CP-tree method, the results show that the proposed method is substantially faster, able to handle higher-dimensional datasets and to prune more non-minimal JEPs.||en|
|dc.description.statementofresponsibility||Quanzhong Liu, Peng Shi, Zhengguo Hu and Yang Zhang||en|
|dc.publisher||Taylor & Francis||en|
|dc.rights||© 2014 Taylor & Francis||en|
|dc.subject||data mining; strong jumping emerging patterns; BSC-tree||en|
|dc.title||A novel approach of mining strong jumping emerging patterns based on BSC-tree||en|
|pubs.library.collection||Electrical and Electronic Engineering publications||en|
|dc.identifier.orcid||Shi, P. [0000-0001-8218-586X]||en|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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