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Chapter 27
Handbook of Research on Global Competitive Advantage through Innovation and Entrepreneurship
Clustering algorithm to identify the number and shape of clusters.
Published in Chapter:
Clustering Global Entrepreneurship through Data Mining Technique
Paula Odete Fernandes (Polytechnic Institute of Bragança, Portugal) and Rui Pedro Lopes (Polytechnic Institute of Bragança, Portugal)
Copyright: © 2015 |Pages: 13
DOI: 10.4018/978-1-4666-8348-8.ch027
Abstract
The purpose of this chapter is to contribute for the identification of groups of countries that share similar patterns regarding the characteristics of Global Entrepreneurship and capturing features of entrepreneurship by focusing on entrepreneurial attitudes and entrepreneurial activity. For this purpose, 67 countries from 2013 GEM survey were selected, and Data Mining Methodology was used. In particular, evolutionary computation is used to determine a finite set of categories to describe the data set according to multi-dimensional similarities among its objects. In other words, several clustering algorithms where used, to get the best categories possible. The results show four clusters with different entrepreneurial attitudes among the countries - very high, medium and low entrepreneurial attitudes and entrepreneurial activities.
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Chapter 40
An Introduction to Clustering Algorithms in Big Data
It’s an algorithm based on grouping together points that are close to each other based on a distance measurement (usually Euclidean distance) and a minimum number of points.
Published in Chapter: An Introduction to Clustering Algorithms in Big Data; From: Encyclopedia of Information Science and Technology, Fifth Edition
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