Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis

Md. Shamim Reza; Sabba Ruhi

doi:doi:10.11648/j.ajtas.20150405.11

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Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis

Md. Shamim Reza, Sabba Ruhi

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 5)

Received: 5 July 2015 Accepted: 17 July 2015 Published: 28 July 2015

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Abstract

For last two decades, clustering is well-recognized area in the research field of data mining. Data clustering plays the major research at pattern recognition, Signal processing, bioinformatics and Artificial Intelligence. Clustering process is an unsupervised learning techniques where it generates a group of object based on their similarity in such a way that the objects belonging to other groups are similar and those belonging to other are dissimilar. This paper analysis the three different data types clustering techniques like K-Means, Principal components analysis (PCA) and Independent component analysis (ICA) in real and simulated data. The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in data clustering, outlier detection and multivariate data visualization. Accurate identification of data clustering plays an important role in statistical analysis. In this paper we explore the connection among these three techniques to identify multivariate data clustering and develop a new method k-means on PCA or ICA then the result shows that ICA based clustering performs well than others.

Published in	American Journal of Theoretical and Applied Statistics (Volume 4, Issue 5)
DOI	10.11648/j.ajtas.20150405.11
Page(s)	317-321
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Clustering, K-means, PCA, ICA

References

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APA Style

Md. Shamim Reza, Sabba Ruhi. (2015). Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis. American Journal of Theoretical and Applied Statistics, 4(5), 317-321. https://doi.org/10.11648/j.ajtas.20150405.11

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ACS Style

Md. Shamim Reza; Sabba Ruhi. Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis. Am. J. Theor. Appl. Stat. 2015, 4(5), 317-321. doi: 10.11648/j.ajtas.20150405.11

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AMA Style

Md. Shamim Reza, Sabba Ruhi. Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis. Am J Theor Appl Stat. 2015;4(5):317-321. doi: 10.11648/j.ajtas.20150405.11

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@article{10.11648/j.ajtas.20150405.11,
  author = {Md. Shamim Reza and Sabba Ruhi},
  title = {Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {5},
  pages = {317-321},
  doi = {10.11648/j.ajtas.20150405.11},
  url = {https://doi.org/10.11648/j.ajtas.20150405.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.11},
  abstract = {For last two decades, clustering is well-recognized area in the research field of data mining. Data clustering plays the major research at pattern recognition, Signal processing, bioinformatics and Artificial Intelligence. Clustering process is an unsupervised learning techniques where it generates a group of object based on their similarity in such a way that the objects belonging to other groups are similar and those belonging to other are dissimilar. This paper analysis the three different data types clustering techniques like K-Means, Principal components analysis (PCA) and Independent component analysis (ICA) in real and simulated data. The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in data clustering, outlier detection and multivariate data visualization. Accurate identification of data clustering plays an important role in statistical analysis. In this paper we explore the connection among these three techniques to identify multivariate data clustering and develop a new method k-means on PCA or ICA then the result shows that ICA based clustering performs well than others.},
 year = {2015}
}

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TY  - JOUR
T1  - Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis
AU  - Md. Shamim Reza
AU  - Sabba Ruhi
Y1  - 2015/07/28
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150405.11
DO  - 10.11648/j.ajtas.20150405.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 317
EP  - 321
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150405.11
AB  - For last two decades, clustering is well-recognized area in the research field of data mining. Data clustering plays the major research at pattern recognition, Signal processing, bioinformatics and Artificial Intelligence. Clustering process is an unsupervised learning techniques where it generates a group of object based on their similarity in such a way that the objects belonging to other groups are similar and those belonging to other are dissimilar. This paper analysis the three different data types clustering techniques like K-Means, Principal components analysis (PCA) and Independent component analysis (ICA) in real and simulated data. The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in data clustering, outlier detection and multivariate data visualization. Accurate identification of data clustering plays an important role in statistical analysis. In this paper we explore the connection among these three techniques to identify multivariate data clustering and develop a new method k-means on PCA or ICA then the result shows that ICA based clustering performs well than others.
VL  - 4
IS  - 5
ER  -

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Author Information

Md. Shamim Reza

Department of Mathematics, Pabna University of Science & Technology, Pabna, Bangladesh
Sabba Ruhi

Department of Mathematics, Pabna University of Science & Technology, Pabna, Bangladesh

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APA Style

Md. Shamim Reza, Sabba Ruhi. (2015). Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis. American Journal of Theoretical and Applied Statistics, 4(5), 317-321. https://doi.org/10.11648/j.ajtas.20150405.11

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ACS Style

Md. Shamim Reza; Sabba Ruhi. Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis. Am. J. Theor. Appl. Stat. 2015, 4(5), 317-321. doi: 10.11648/j.ajtas.20150405.11

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AMA Style

Md. Shamim Reza, Sabba Ruhi. Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis. Am J Theor Appl Stat. 2015;4(5):317-321. doi: 10.11648/j.ajtas.20150405.11

Copy | Download

@article{10.11648/j.ajtas.20150405.11,
  author = {Md. Shamim Reza and Sabba Ruhi},
  title = {Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {5},
  pages = {317-321},
  doi = {10.11648/j.ajtas.20150405.11},
  url = {https://doi.org/10.11648/j.ajtas.20150405.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.11},
  abstract = {For last two decades, clustering is well-recognized area in the research field of data mining. Data clustering plays the major research at pattern recognition, Signal processing, bioinformatics and Artificial Intelligence. Clustering process is an unsupervised learning techniques where it generates a group of object based on their similarity in such a way that the objects belonging to other groups are similar and those belonging to other are dissimilar. This paper analysis the three different data types clustering techniques like K-Means, Principal components analysis (PCA) and Independent component analysis (ICA) in real and simulated data. The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in data clustering, outlier detection and multivariate data visualization. Accurate identification of data clustering plays an important role in statistical analysis. In this paper we explore the connection among these three techniques to identify multivariate data clustering and develop a new method k-means on PCA or ICA then the result shows that ICA based clustering performs well than others.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis
AU  - Md. Shamim Reza
AU  - Sabba Ruhi
Y1  - 2015/07/28
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150405.11
DO  - 10.11648/j.ajtas.20150405.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 317
EP  - 321
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150405.11
AB  - For last two decades, clustering is well-recognized area in the research field of data mining. Data clustering plays the major research at pattern recognition, Signal processing, bioinformatics and Artificial Intelligence. Clustering process is an unsupervised learning techniques where it generates a group of object based on their similarity in such a way that the objects belonging to other groups are similar and those belonging to other are dissimilar. This paper analysis the three different data types clustering techniques like K-Means, Principal components analysis (PCA) and Independent component analysis (ICA) in real and simulated data. The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in data clustering, outlier detection and multivariate data visualization. Accurate identification of data clustering plays an important role in statistical analysis. In this paper we explore the connection among these three techniques to identify multivariate data clustering and develop a new method k-means on PCA or ICA then the result shows that ICA based clustering performs well than others.
VL  - 4
IS  - 5
ER  -

Copy | Download