Cluster analysis: Difference between revisions

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Networks of economic entities, such as industrial enterprises and common production cycles, and organizations that provide support services (banks, consulting and marketing firms, research and educational institutions, insurance companies) form clusters, a '''complex economic system'''.  
'''Cluster analysis''' is an explorative procedure to divide data sets into groups with regard to their similarity. Various criteria and characteristics can be used for cluster analysis, on the basis of which the similarity of the individual data is determined. A cluster analysis is based on the calculation of a similarity measure and belong to the unsupervised machine learning methods.  There are numerous algorithms for dividing data into clusters. Which method is most suitable generally depends on the question. Often, the results of different methods are compared at the end to determine the correct method.  
Over the past decade, cluster policy has become one of the most important focal points of national policy in developed and developing countries to enhance national and regional competitiveness.
This idea is spreading in the form of clearly defined policies and other policy initiatives such as regional strategies and activities supporting local production systems.


Most of today's industrialized economies need the institutional support of firms to become more competitive.
== Prerequisites of the cluster analysis ==
In Afanasiev M., Korchagina N., and Myasnikova L. (2006) is argued that enterprise consolidation and clustering are currently one of the most effective supports for increasing production efficiency.
A cluster should be maximally homogeneous within itself and clearly distinguishable from other clusters. A clear demarcation must be ensured. Therefore, the following conditions should be met:
The global economy has been impacted by trends in the cluster's role expansion.
* '''Size of the data set:''' Under certain circumstances, a meaningful result can only be achieved with a sufficiently large data set. Depending on the task, it is therefore necessary to weigh up whether the amount of data is sufficient.  
Innovative approaches to creating integrated management forms are required for the modern evolution of economic space across the globe, taking into account factors such as:
* '''Normalization of the data:''' if there are large differences in the value range of the data, the data should be normalized beforehand.  
* Internal and external regionalisation factors include enhancing regional and national competitiveness.
* '''Elimination of outliers:''' outliers can strongly distort the results. Thus, the data should first be analyzed and evaluated for possible extreme values and outliers should then be eliminated.  
* An increase in regional investments and innovation.
* '''Bias:''' If there are strong correlations between the data, the results could end up being heavily biased. This must be avoided.
* Development of long-term forms of economic and territorial integration.
* Enhancement of regional and national competitiveness.  
* A rise in globalization processes.
 
Therefore, based on the proactive promotion of propellant industries, the cluster principle becomes more relevant in terms of creating clusters of growth poles in the regional economy and increasing the effectiveness of public policy.
 
 
 
{{a|Francesca Scattolin}}
[[Category:Economics]]

Revision as of 13:24, 22 November 2022

Cluster analysis is an explorative procedure to divide data sets into groups with regard to their similarity. Various criteria and characteristics can be used for cluster analysis, on the basis of which the similarity of the individual data is determined. A cluster analysis is based on the calculation of a similarity measure and belong to the unsupervised machine learning methods. There are numerous algorithms for dividing data into clusters. Which method is most suitable generally depends on the question. Often, the results of different methods are compared at the end to determine the correct method.

Prerequisites of the cluster analysis

A cluster should be maximally homogeneous within itself and clearly distinguishable from other clusters. A clear demarcation must be ensured. Therefore, the following conditions should be met:

  • Size of the data set: Under certain circumstances, a meaningful result can only be achieved with a sufficiently large data set. Depending on the task, it is therefore necessary to weigh up whether the amount of data is sufficient.
  • Normalization of the data: if there are large differences in the value range of the data, the data should be normalized beforehand.
  • Elimination of outliers: outliers can strongly distort the results. Thus, the data should first be analyzed and evaluated for possible extreme values and outliers should then be eliminated.
  • Bias: If there are strong correlations between the data, the results could end up being heavily biased. This must be avoided.
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