Sequence Dataset Similarity Measure by Aggregated Shared Emerging Sequences

DOI

10.2991/csss-14.2014.49How to use a DOI?

Keywords

data mining; aggregated shared emerging sequences; similarity measure

Abstract

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.

Copyright

© 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/).

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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  -