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dc.contributor.authorLiu, Q.en
dc.contributor.authorShi, P.en
dc.contributor.authorHu, Z.en
dc.contributor.authorZhang, Y.en
dc.identifier.citationInternational Journal of Systems Science, 2014; 45(3):598-615en
dc.description.abstractIt 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.statementofresponsibilityQuanzhong Liu, Peng Shi, Zhengguo Hu and Yang Zhangen
dc.publisherTaylor & Francisen
dc.rights© 2014 Taylor & Francisen
dc.subjectdata mining; strong jumping emerging patterns; BSC-treeen
dc.titleA novel approach of mining strong jumping emerging patterns based on BSC-treeen
dc.typeJournal articleen
pubs.library.collectionElectrical and Electronic Engineering publicationsen
dc.identifier.orcidShi, P. [0000-0001-8218-586X]en
Appears in Collections:Electrical and Electronic Engineering publications

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