IDENTIFYING HIGH POTENTIAL ENTREPRENEURS IN A DEVELOPING COUNTRY: A CLUSTER ANALYSIS OF UGANDAN ENTREPRENEURS

2013 ◽  
Vol 18 (02) ◽  
pp. 1350010 ◽  
Author(s):  
ARTHUR SSERWANGA ◽  
GERRIT ROOKS
Keyword(s):  
Cluster Analysis ◽  
Developing Countries ◽  
Developing Country ◽  
Opportunity Recognition ◽  
High Potential ◽  
Homogeneous Groups ◽  
Entrepreneurial Activities ◽  
Systematic Knowledge

It has often been argued that entrepreneurs in developing countries can be classified as either "survival" or "growth-oriented." However, there is little systematic knowledge about classification of entrepreneurs in developing countries. We propose that what we call high potential entrepreneurs can be distinguished from low potential entrepreneurs, given that high potential entrepreneurs recognize and effectively exploit opportunities. In this paper we classify entrepreneurs using three core entrepreneurial activities; opportunity recognition, planning and innovativeness. A cluster analysis of about 700 Ugandan entrepreneurs yielded two natural, distinct and internally homogeneous groups of high potential and low potential entrepreneurship.

Application of optimization tasks in cluster analysis

10.12737/7483 ◽  
2014 ◽  
Vol 8 (7) ◽  
pp. 0-0
Author(s):  
Олег Сдвижков ◽  
Oleg Sdvizhkov
Keyword(s):  
Cluster Analysis ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Number Of Clusters ◽  
Homogeneous Groups ◽  
Finite Set ◽  
Minimum Number

Cluster analysis [3] is a relatively new branch of mathematics that studies the methods partitioning a set of objects, given a finite set of attributes into homogeneous groups (clusters). Cluster analysis is widely used in psychology, sociology, economics (market segmentation), and many other areas in which there is a problem of classification of objects according to their characteristics. Clustering methods implemented in a package STATISTICA [1] and SPSS [2], they return the partitioning into clusters, clustering and dispersion statistics dendrogram of hierarchical clustering algorithms. MS Excel Macros for main clustering methods and application examples are given in the monograph [5]. One of the central problems of cluster analysis is to define some criteria for the number of clusters, we denote this number by K, into which separated are a given set of objects. There are several dozen approaches [4] to determine the number K. In particular, according to [6], the number of clusters K - minimum number which satisfies where - the minimum value of total dispersion for partitioning into K clusters, N - number of objects. Among the clusters automatically causes the consistent application of abnormal clusters [4]. In 2010, proposed and experimentally validated was a method for obtaining the number of K by applying the density function [4]. The article offers two simple approaches to determining K, where each cluster has at least two objects. In the first number K is determined by the shortest Hamiltonian cycles in the second - through the minimum spanning tree. The examples of clustering with detailed step by step solutions and graphic illustrations are suggested. Shown is the use of macro VBA Excel, which returns the minimum spanning tree to the problems of clustering. The article contains a macro code, with commentaries to the main unit.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

2007 ◽  
Vol 38 (3) ◽  
pp. 303-314 ◽  
Author(s):  
K. Srinivasa Raju ◽  
D. Nagesh Kumar
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Artificial Neural Networks ◽  
Optimal Number ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Homogeneous Groups ◽  
Index Approach ◽  
Artificial Neural

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies–Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Cluster Analysis of Gallstone FT-IR Spectra: Tests on Simulated Mixture Spectra and Comparison between Spectral and Morphological Classification of Human Gallstones

1998 ◽  
Vol 52 (9) ◽  
pp. 1210-1221 ◽  
Author(s):  
Eric Laloum ◽  
Nguyen Quy Dao ◽  
Michel Daudon
Keyword(s):  
Cluster Analysis ◽  
Ir Spectra ◽  
Black Pigment ◽  
Brown Pigment ◽  
Morphological Classification ◽  
Single Linkage ◽  
Homogeneous Groups ◽  
Ft Ir ◽  
Pure Cholesterol

Sixty-four combination spectra of three major gallstone components [i.e., cholesterol, calcium bilirubinate, and calcium carbonate (aragonite)] were simulated in accordance with a “fractal” ternary diagram. Comparison between the original pattern of composition and factorial maps of pretreated spectra makes it possible to show the effects of different normalization procedures (Euclidean norm, spectrum maximum, and area under spectrum set to 1). Cluster analysis of these spectra, depending on different agglomerative links (single linkage, complete linkage, average linkage, and Ward's criterion), was carried out. All the resultant trees yield the same groups, but Ward's criterion best preserves the pattern of the data. More than 100 gallstones from France and Vietnam were classified by using cluster analysis of their FT-IR spectra with Ward's criterion. Seven homogeneous groups of spectra were extracted, which have been significantly correlated to the four morphological types of gallstones: pure cholesterol, mixed cholesterol, brown pigment, and black pigment stones. This analysis also reveals that the morphological groups are not homogeneous in composition, in particular for black pigment stones.

scholarly journals Relationship between school refusal behavior and social functioning: a cluster analysis approach

2019 ◽  
Vol 12 (1) ◽  
pp. 17 ◽  
Author(s):  
Carolina Gonzálvez ◽  
Cándido J. Inglés ◽  
Christopher A. Kearney ◽  
Ricardo Sanmartín ◽  
María Vicent ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Social Functioning ◽  
Adaptive Functioning ◽  
Functional Model ◽  
School Refusal ◽  
Assessment Scale ◽  
Analysis Approach ◽  
Homogeneous Groups ◽  
School Refusal Behavior

On the basis of the heterogeneous casuistry that characterizes the students who refuse going to school, it is useful to have a classification of this population in homogeneous groups. For this, the aim of this study was, first, to identify by cluster analysis the profiles of school refusal behavior based on the functional model evaluated through the School Refusal Assessment Scale-Revised (SRAS-S). Secondly, it is intended to analyze if there are differences in social functioning scores according to the school refusal profiles identified. This study involved 1212 Spanish children between 8 and 11 years old (M=9.12, SD=1.05) who completed the SRAS-R to evaluate the school refusal behavior and the Child and Adolescent Social Adaptive Functioning Scale (CASAFS) to assess social functioning. Four profiles were identified: Non-school refusers, School refusers by mixed reinforcements, School refusers by tangible reinforcements and School refusers by negative reinforcements. The profile of Non-school refusers achieved the highest average scores in social functioning, while School refusers by mixed reinforcements group obtained the lowest average scores in social functioning. In general, the profiles found support the clusters identified in previous studies. The implications of social functioning on school refusal behavior are discussed.

Neurophysiologic classification of Wilson's disease by using cluster analysis

2006 ◽  
Vol 37 (01) ◽  
Author(s):  
W Hermann ◽  
T Villmann ◽  
HJ Kühn ◽  
P Baum ◽  
G Reichel ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Wilson’S Disease ◽  
Wilson's Disease

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

A Classification of 148 U.S. Maize Inbreds: II. Validation of Cluster Analysis Based on RFLPs

Crop Science ◽  
1994 ◽  
Vol 34 (4) ◽  
pp. 852-865 ◽  
Author(s):  
Rita Hogan Mumm ◽  
Lawrence J. Hubert ◽  
J. W. Dudley
Keyword(s):  
Cluster Analysis ◽  
Maize Inbreds

Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

2011 ◽  
Vol 8 (1) ◽  
pp. 201-210
Author(s):  
R.M. Bogdanov
Keyword(s):  
Cluster Analysis ◽  
Defect Density ◽  
Density Variation ◽  
Distance Functions ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Classification Of Images ◽  
Main Oil Pipeline ◽  
Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

Maximizing Utility vis-à-vis Developing Countries

2018 ◽  
Author(s):  
Sarah Blodgett Bermeo
Keyword(s):  
Developing Countries ◽  
Developing Country ◽  
Formal Model ◽  
Policy Goals ◽  
The Government ◽  
Development Outcomes

This chapter develops a formal model of targeted development. It starts from the assumption that governments in industrialized states seek to maximize their own utility in interactions with developing countries. Development concerns compete with other policy goals for scarce government resources. The level of development resources an industrialized country government targets to a particular developing country depends on the weight the government places on development in that country as well as the efficiency of the country in turning resources into development outcomes that the industrialized state values. One of the key insights of the model is that, as governments work to maximize the utility gained per dollar (or euro, yen, etc.) spent, development motives will influence policy in multiple issue areas. The chapter also draws out implications of the theory for each of the issue areas examined in the empirical chapters.

Liberal development, 1925–46

2017 ◽  
Author(s):  
John Toye
Keyword(s):  
Developing Countries ◽  
Economic Development ◽  
Exchange Rate ◽  
Developing Country ◽  
Trade Protection ◽  
Communist Country ◽  
Economic Policies ◽  
Government Finance ◽  
Soviet Government ◽  
Experimental Economic

Keynes’s writings are often disregarded in the context of economic development, overlooking that Russia was a developing country in his lifetime. He wrote about the experimental economic techniques that the Soviet government employed. He visited Russia three times and wrote A Short View of Russia in which he explained and criticized Bolsheviks’ policy of export and import monopolies, an overvalued exchange rate, inflationary government finance, and the subsidization of industry. These were policies that many developing countries adopted after decolonization. Keynes’s conclusion was that they were inefficient and that ‘bourgeois economics was valid in a communist country’. Did Keynes change his mind in the 1930s? If anything, he grew more harshly critical of Soviet economic policies and carefully distinguished them from his own endorsement of moderate trade protection and government supplementary investment in times of depression.

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