NDLI: Feature reduction using principal component analysis for agricultural data set

Content Provider	IEEE Xplore Digital Library
Author	Mishra, S. Mishra, D. Das, S. Rath, A.K.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science and Engineering, CEB, Odisha, India (Das, S.; Rath, A.K.) \|\| Institute of Technical Education & Research, Siksha O Anusandhan University, Bhubaneswar, India (Mishra, D.) \|\| Department of Computer Sc. & Application, Center for Post Graduate Studies, OUAT, Bhubaneswar India (Mishra, S.)
Abstract	Many applications like video surveillance, telecommunication, weather forecasting and sensor networks uses high volume of data of different types. The effective and efficient analysis of data in such different forms becomes a challenging task. Analysis of such large expression data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of attributes. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. In this paper, we have proposed to use the method of k-means clustering and principal component analysis (PCA) approach for attribute reduction, which initially apply PCA to obtain reduced uncorrelated attributes specifying maximal eigenvalues in the dataset with minimum loss of information. Then again we proposed to use k-means on the PCA reduced dataset to discover discriminative features that will be the most adequate ones for classification. This is a combination of clustering approach with feature reduction to obtain a minimal set attributes retaining a suitably high accuracy in representing the original features. We have used the Greengram agricultural data set. Finally, we found that the result of clustering is same after reducing the attributes using PCA.
Starting Page	209
Ending Page	213
File Size	307087
Page Count	5
File Format	PDF
ISBN	9781424486786
e-ISBN	9781424486793
DOI	10.1109/ICECTECH.2011.5941686
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-08
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Clustering algorithms k-means Feature extraction features extraction Agriculture Data mining Synthetic aperture sonar principal component analysis Principal component analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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