Welcome to the InfoSci Platform
Cluster Analysis: A Statistical Approach for E-Governance for Better Policy Decisions

Cluster Analysis: A Statistical Approach for E-Governance for Better Policy Decisions

Pankaj Nagar
Copyright: © 2014 |Pages: 37
ISBN13: 9781466651463|ISBN10: 1466651466|EISBN13: 9781466651470
DOI: 10.4018/978-1-4666-5146-3.ch006
Cite Chapter Cite Chapter

MLA

Nagar, Pankaj. "Cluster Analysis: A Statistical Approach for E-Governance for Better Policy Decisions." Governometrics and Technological Innovation for Public Policy Design and Precision, edited by Sangeeta Sharma, et al., IGI Global, 2014, pp. 123-159. https://doi.org/10.4018/978-1-4666-5146-3.ch006

APA

Nagar, P. (2014). Cluster Analysis: A Statistical Approach for E-Governance for Better Policy Decisions. In S. Sharma, P. Nagar, & I. Sodhi (Eds.), Governometrics and Technological Innovation for Public Policy Design and Precision (pp. 123-159). IGI Global. https://doi.org/10.4018/978-1-4666-5146-3.ch006

Chicago

Nagar, Pankaj. "Cluster Analysis: A Statistical Approach for E-Governance for Better Policy Decisions." In Governometrics and Technological Innovation for Public Policy Design and Precision, edited by Sangeeta Sharma, Pankaj Nagar, and Inderjeet Singh Sodhi, 123-159. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-5146-3.ch006

Export Reference

Mendeley
Favorite

Abstract

The cluster analysis, also known as grouping, clumping, unsupervised classification, is one of the multivariate analysis techniques. The technique of cluster analysis is highly useful in a wide range of problems related to managerial decisions, psychological solutions, categorization of business organizations on the basis of their performance for constructing separate policies for each clusters, in health sectors, societal problems, etc. For good governance there is a need to apply the proper statistical tools with ICT. Even today, the statistical tools are rarely used in the region of e-governance for better policy development. This chapter discusses the use of cluster analysis in classifying a large amount of data into sub-groups (known as clusters), which are homogeneous in a certain sense, and analyzes each sub-group separately to find solutions for each of them. The method in explained with the help of an illustration, by using the SPSS software.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.