Multivariate data classification using PolSOM

Lu Xu; Tommy W. S. Chow

doi:10.1109/PHM.2011.5939556

Multivariate data classification using PolSOM

Lu Xu
, Tommy W. S. Chow

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

18 Citations (Scopus)

Abstract

Polar self-organizing map (PolSOM), a novel data visualization algorithm, projects data on a polar map with two variables, radius and angle, which represent data weight and feature respectively. Compared with self-organizing map (SOM), which is a traditional method for dimensionality reduction and data classification, PolSOM visualizes not only the inter-neuron distance, but also the differences among clusters in terms of weight and feature. PolSOM sets each neuron as a benchmark to group the similar data together, and reflects the data characteristic by their polar coordinates. In this paper, two multivariate data sets are provided to demonstrate the performance of PolSOM. All simulations are compared with SOM and ViSOM. © 2011 IEEE.

Original language	English
Title of host publication	2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011
DOIs	https://doi.org/10.1109/PHM.2011.5939556
Publication status	Published - 2011
Event	2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011 - Shenzhen, China Duration: 24 May 2011 → 25 May 2011

Conference

Conference	2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011
Place	China
City	Shenzhen
Period	24/05/11 → 25/05/11

Research Keywords

Classification
Polar self-organizing map (PolSOM)
Self-organizing map (SOM)
Visualization

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10.1109/PHM.2011.5939556

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@inproceedings{6aabdff5de734300900a37138f5207e1,

title = "Multivariate data classification using PolSOM",

abstract = "Polar self-organizing map (PolSOM), a novel data visualization algorithm, projects data on a polar map with two variables, radius and angle, which represent data weight and feature respectively. Compared with self-organizing map (SOM), which is a traditional method for dimensionality reduction and data classification, PolSOM visualizes not only the inter-neuron distance, but also the differences among clusters in terms of weight and feature. PolSOM sets each neuron as a benchmark to group the similar data together, and reflects the data characteristic by their polar coordinates. In this paper, two multivariate data sets are provided to demonstrate the performance of PolSOM. All simulations are compared with SOM and ViSOM. {\textcopyright} 2011 IEEE.",

keywords = "Classification, Polar self-organizing map (PolSOM), Self-organizing map (SOM), Visualization",

author = "Lu Xu and Chow, \{Tommy W. S.\}",

year = "2011",

doi = "10.1109/PHM.2011.5939556",

language = "English",

isbn = "9781424479511",

booktitle = "2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011",

note = "2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011 ; Conference date: 24-05-2011 Through 25-05-2011",

}

TY - GEN

T1 - Multivariate data classification using PolSOM

AU - Xu, Lu

AU - Chow, Tommy W. S.

PY - 2011

Y1 - 2011

N2 - Polar self-organizing map (PolSOM), a novel data visualization algorithm, projects data on a polar map with two variables, radius and angle, which represent data weight and feature respectively. Compared with self-organizing map (SOM), which is a traditional method for dimensionality reduction and data classification, PolSOM visualizes not only the inter-neuron distance, but also the differences among clusters in terms of weight and feature. PolSOM sets each neuron as a benchmark to group the similar data together, and reflects the data characteristic by their polar coordinates. In this paper, two multivariate data sets are provided to demonstrate the performance of PolSOM. All simulations are compared with SOM and ViSOM. © 2011 IEEE.

AB - Polar self-organizing map (PolSOM), a novel data visualization algorithm, projects data on a polar map with two variables, radius and angle, which represent data weight and feature respectively. Compared with self-organizing map (SOM), which is a traditional method for dimensionality reduction and data classification, PolSOM visualizes not only the inter-neuron distance, but also the differences among clusters in terms of weight and feature. PolSOM sets each neuron as a benchmark to group the similar data together, and reflects the data characteristic by their polar coordinates. In this paper, two multivariate data sets are provided to demonstrate the performance of PolSOM. All simulations are compared with SOM and ViSOM. © 2011 IEEE.

KW - Classification

KW - Polar self-organizing map (PolSOM)

KW - Self-organizing map (SOM)

KW - Visualization

UR - https://www.scopus.com/pages/publications/79960927459

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-79960927459&origin=recordpage

U2 - 10.1109/PHM.2011.5939556

DO - 10.1109/PHM.2011.5939556

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424479511

BT - 2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011

T2 - 2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011

Y2 - 24 May 2011 through 25 May 2011

ER -

Multivariate data classification using PolSOM

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