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Proceedings Paper

Mining strong jumping emerging patterns with a novel list data structure
Author(s): Xiangtao Chen; Ziping Guan
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Paper Abstract

Strong Jumping Emerging Patterns (SJEPs) are data mining patterns which have strong discriminating abilities in classification. However, SJEPs mining algorithms in current years are usually achieved by the data structure, tree. These existing algorithms using the tree structure are difficult to achieve excellent performance. In this paper, we propose a novel method of mining SJEPs named PPSJEP. This algorithm is based on a novel data structure called NSJEP-list, which is improved from the N-list. We use the NSJEP-lists to replace the tree structure. First, we get the individual items’ NSJEP-lists from the tree. Then we use the intersection of NSJEP-lists to get the longer itemsets’ NSJEP-lists which includes the information of the position and the count in each class. And we mine the SJEPs through the information. Experiments are performed on six UCI datasets. Compared with existing algorithm in running time and classification accuracy, the experimental results show that our algorithm uses less time to mine SJEPs and get the same classification accuracy, especially in lower minimum support threshold.

Paper Details

Date Published: 19 June 2017
PDF: 8 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431M (19 June 2017);
Show Author Affiliations
Xiangtao Chen, Hunan Univ. (China)
Ziping Guan, Hunan Univ. (China)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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