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

R. Krishnan; Jagannathan Sarangapani

doi:10.1109/SSCI.2015.82

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

R. Krishnan
, Jagannathan Sarangapani

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence (2015, Cape Town, South Africa)
DOIs	https://doi.org/10.1109/SSCI.2015.82
State	Published - Dec 1 2015

Keywords

Artificial intelligence
Big data
Hier-archical clustering
Hierarchical clustering approach
Mahalanobis
Mahalanobis distances
Systems engineering

Disciplines

Electrical and Computer Engineering

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10.1109/SSCI.2015.82License: Unspecified

https://doi.org/10.1109/SSCI.2015.82

Cite this

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title = "Hierarchical Mahalanobis Distance Clustering based Technique for Prognostics in Applications Generating Big Data",

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.",

keywords = "Artificial intelligence, Big data, Hier-archical clustering, Hierarchical clustering approach, Mahalanobis, Mahalanobis distances, Systems engineering",

author = "R. Krishnan and Jagannathan Sarangapani",

year = "2015",

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doi = "10.1109/SSCI.2015.82",

language = "American English",

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AU - Sarangapani, Jagannathan

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N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Big data

KW - Hier-archical clustering

KW - Hierarchical clustering approach

KW - Mahalanobis

KW - Mahalanobis distances

KW - Systems engineering

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/3906

UR - https://doi.org/10.1109/SSCI.2015.82

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DO - 10.1109/SSCI.2015.82

M3 - Article

JO - Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence (2015, Cape Town, South Africa)

JF - Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence (2015, Cape Town, South Africa)

ER -

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

Abstract

Keywords

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