Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/89647
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Type: Journal article
Title: A novel approach of mining strong jumping emerging patterns based on BSC-tree
Author: Liu, Q.
Shi, P.
Hu, Z.
Zhang, Y.
Citation: International Journal of Systems Science, 2014; 45(3):598-615
Publisher: Taylor & Francis
Issue Date: 2014
ISSN: 0020-7721
1464-5319
Statement of
Responsibility: 
Quanzhong Liu, Peng Shi, Zhengguo Hu and Yang Zhang
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.
Keywords: data mining; strong jumping emerging patterns; BSC-tree
Rights: © 2014 Taylor & Francis
DOI: 10.1080/00207721.2012.724110
Appears in Collections:Aurora harvest 2
Electrical and Electronic Engineering publications

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