Article
Hierarchical Mahalanobis Distance Clustering based Technique for Prognostics in Applications Generating Big Data
Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence (2015, Cape Town, South Africa)
Abstract
In this paper, a Mahalanobis Distance (MD) based hierarchical clustering technique is proposed for prognostics in applications generating Big Data. This technique is shown to have the ability to overcome certain challenges concerning Big Data analysis. In this technique, Mahalanobis Taguchi Strategy (MTS) is utilized to organize the MD values into a tree. The hierarchical clustering approach is then applied to obtain an overall MD value which is trended over time for prediction. Simulation results are presented to demonstrate the efficiency of the proposed technique.
Meeting Name
2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015 (2015: Dec. 7-10, Cape Town, South Africa)
Department(s)
Electrical and Computer Engineering
Comments
This research was supported in part by NSF I/UCRC award IIP 1134721 and Intelligent Systems Center.
Keywords and Phrases
- Artificial intelligence,
- Systems engineering,
- Hier-archical clustering,
- Hierarchical clustering approach,
- Mahalanobis,
- Mahalanobis distances,
- Big data
International Standard Book Number (ISBN)
978-1-4799-7560-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
12-1-2015
Publication Date
01 Dec 2015
Disciplines
Citation Information
R. Krishnan and Jagannathan Sarangapani. "Hierarchical Mahalanobis Distance Clustering based Technique for Prognostics in Applications Generating Big Data" Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence (2015, Cape Town, South Africa) (2015) p. 516 - 521 Available at: http://works.bepress.com/jagannathan-sarangapani/209/