Sequence Dataset Similarity Measure by Aggregated Shared Emerging Sequences
Chen Xiangtao, Wang Jing, Ding Pingjian
Available Online June 2014.
- 10.2991/csss-14.2014.49How to use a DOI?
- data mining; aggregated shared emerging sequences; similarity measure
Emerging sequences (ESs) represent some strong distinguishing knowledge and are very useful for building powerful classifiers. The shared emerging sequences (SESs) are some emerging sequences shared by two or more datasets, which show great values in dataset similarity measure. As for the application of SESs, in this paper, an aggregated SESs based similarity measure strategy is introduced to calculate the similarity of two datasets. Experiments are conducted to analyze the similarity evaluation ability of aggregated SESs, and to verify its effectiveness by auxiliary classification. Experimental results show that our proposed method is of good performance.
- © 2014, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Chen Xiangtao AU - Wang Jing AU - Ding Pingjian PY - 2014/06 DA - 2014/06 TI - Sequence Dataset Similarity Measure by Aggregated Shared Emerging Sequences BT - Proceedings of the 3rd International Conference on Computer Science and Service System PB - Atlantis Press SP - 211 EP - 214 SN - 1951-6851 UR - https://doi.org/10.2991/csss-14.2014.49 DO - 10.2991/csss-14.2014.49 ID - Xiangtao2014/06 ER -