Hierarchical Mahalanobis Distance Clustering based Technique for Prognostics in Applications Generating Big Data

Author

R. Krishnan
Jagannathan Sarangapani, Missouri University of Science and TechnologyFollow

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.

Recommended Citation

R. Krishnan and J. 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), pp. 516 - 521, Institute of Electrical and Electronics Engineers (IEEE), Dec 2015.

The definitive version is available at https://doi.org/10.1109/SSCI.2015.82

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

01 Dec 2015