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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 |
Published version: | http://dx.doi.org/10.1080/00207721.2012.724110 |
Appears in Collections: | Aurora harvest 2 Electrical and Electronic Engineering publications |
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