Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/80824
Type: | Journal article |
Title: | Fast algorithms for mining Strong Jumping Emerging Patterns using the contrast pattern tree |
Author: | Liu, Q. Shi, P. Hu, Z. |
Citation: | ICIC Express Letters, Part B: Applications, 2013; 4(1):121-128 |
Publisher: | ICIC International |
Issue Date: | 2013 |
ISSN: | 2185-2766 |
Statement of Responsibility: | Quanzhong Liu, Peng Shi and Zhengguo Hu |
Abstract: | Efficient mining of Strong Jumping Emerging Patterns (SJEPs) is useful for constructing accurate classifiers. The method for mining SJEPs based on a contrast pattern tree structure (CP-Tree) has been demonstrated to perform extremely well for a low-dimensional dataset. In the method, a large number of non-minimal JEPs are generated during the mining process. So, it is unable to handle higher-dimensional attributes. In this paper, we propose a novel pattern pruning technique that dramatically reduces the search space. The CP-tree method is greatly improved by the proposed pattern pruning technique. Experiments are performed on two high-dimensional cancer datasets. Compared with the original CP-tree algorithm, the results show that the improved CP-tree algorithm is substantially faster, and able to handle higher-dimensional attributes. |
Keywords: | CP-tree Data mining Pattern pruning SJEPs |
Rights: | Copyright status unknown |
Published version: | http://www.ijicic.org/elb-4(1).htm |
Appears in Collections: | Aurora harvest 4 Electrical and Electronic Engineering publications |
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