Hierarchical Mahalanobis Distance Clustering based Technique for Prognostics in Applications Generating Big Data
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.
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
2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015 (2015: Dec. 7-10, Cape Town, South Africa)
Electrical and Computer Engineering
Keywords and Phrases
Artificial intelligence; Systems engineering; Hier-archical clustering; Hierarchical clustering approach; Mahalanobis; Mahalanobis distances; Big data
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Dec 2015