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Application of Factor Analysis to k-means Clustering Algorithm on Transportation Data

by Sesham Anand, P. Padmanabham, A. Govardhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 15
Year of Publication: 2014
Authors: Sesham Anand, P. Padmanabham, A. Govardhan
10.5120/16673-6677

Sesham Anand, P. Padmanabham, A. Govardhan . Application of Factor Analysis to k-means Clustering Algorithm on Transportation Data. International Journal of Computer Applications. 95, 15 ( June 2014), 40-46. DOI=10.5120/16673-6677

@article{ 10.5120/16673-6677,
author = { Sesham Anand, P. Padmanabham, A. Govardhan },
title = { Application of Factor Analysis to k-means Clustering Algorithm on Transportation Data },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 15 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 40-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number15/16673-6677/ },
doi = { 10.5120/16673-6677 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:19:33.354905+05:30
%A Sesham Anand
%A P. Padmanabham
%A A. Govardhan
%T Application of Factor Analysis to k-means Clustering Algorithm on Transportation Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 15
%P 40-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Factor Analysis is a very useful linear algebra technique used for dimensionality reduction. It is also used for data compression and visualization of high dimensional datasets. This technique tries to identify from among a large set of variables, a reduced set of components which summarizes the original data. This is done by identifying groups of variables which have a strong inter correlation. The original variables are transformed into a smaller set of components which have a strong linear correlation. Using several data analysis techniques like Principal Components Analysis (PCA), Factor Analysis, cluster analysis may give insight into the patterns present in the data but may also give different results. The aim of this work is to study the use of Factor Analysis (FA) in capturing the cluster structures from transportation (HIS) data. It is proposed to compare the clustering obtained from original data from that of factor scores. Steps involved in preprocessing the transportation data are also illustrated.

References
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  2. Chris Ding, Xiaofeng He, k-means clustering via principal component analysis, Proceedings of the 21st International Conference on Machine Learning, Banff, Canada, 2004.
  3. Sesham Anand, Sai Hanuman A, Dr. Govardhan A, and Dr. Padmanabham P, Application of Data Mining Techniques to Transportation Demand Modelling Using Home Interview Survey Data, International Conference on Systemics, Cybernetics and Informatics 2008.
  4. Sesham Anand, Dr P. Padmanabham, Dr. A Govardhan, Dr. A. Sai Hanuman, Performance of Clustering Algorithms on Home Interview Survey Data Employed for Travel Demand Estimation, International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 2767-2771
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  7. Sesham Anand, Dr P. Padmanabham, Dr. A Govardhan, Dr. A. Sai Hanuman, Performance of Clustering Algorithms on Home Interview Survey Data Employed for Travel Demand Estimation, International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 2767-2771
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  10. Sesham Anand, Sai Hanuman A, Dr. Govardhan A, and Dr. Padmanabham P, Application of Data Mining Techniques to Transportation Demand Modelling Using Home Interview Survey Data, International Conference on Systemics, Cybernetics and Informatics 2008.
  11. Sesham Anand, Sai Hanuman A, Dr. Govardhan A, and Dr. Padmanabham P, Use of Data Mining Techniques in understanding Home Interview Surveys Employed for Travel Demand Estimation, International Conference on data Mining(DMIN '08) at Las Vegas, USA, 2008
Index Terms

Computer Science
Information Sciences

Keywords

Principal Components Analysis (PCA) Factor Analysis (FA) House Hold Interview Survey(HIS) Data High Dimensional data.

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