A Method for Knowledge Mining of Satellite State Association

DOI

10.2991/icaita-16.2016.17How to use a DOI?

Keywords

spacecraft state association; state association knowledge mining (SAKM); Similar Density Merge Clustering (SDMC); spacecraft abnormal detection

Abstract

This paper presents a new scheme for data mining in spacecraft state association and abnormal detection. A method which includes state association knowledge mining (SAKM) and Similar Density Merge Clustering (SDMC) is developed. Data from the satellite are the most critical thing for analyzing satellite state and abnormal detection, therefore correct analyze of the regulation is quintessential for the detection of satellite abnormal detection. The associations in any of the subsystems are known when satellite was designed; however, the associations between any two of the subsystems are to be found. In such cases a SAKM algorithm becomes essential for obtaining the correct regulation. Clustering algorithm such as K-means is important in the SAKM algorithm, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of K-means method. So that it can be effectively used for regulation discovery of data and satellite abnormal detection. The SDMC approach involves determination of classes’ centers from the classes established by K-means method. In this way the points in one class are close enough. The high similarity points fall into the same class while the low similarity points fall into the different class based on the SDMC algorithm, the SAKM algorithm use Apriori algorithm mining frequent item sets and association rules of parameter feature characters. The experimental results show that as compared to the K-means algorithm the SDMC method can effectively cluster the data, the SAKM algorithm can correctly mining the satellite association knowledge.

Copyright

© 2016, 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  - Jing Zhao
AU  - Tianshe Yang
AU  - Ke Zhang
AU  - Xingmiao Liu
AU  - Na Fu
AU  - Xuhua Wang
PY  - 2016/01
DA  - 2016/01
TI  - A Method for Knowledge Mining of Satellite State Association
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 66
EP  - 69
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.17
DO  - 10.2991/icaita-16.2016.17
ID  - Zhao2016/01
ER  -