Classification of Iran\'s Provinces in Terms of Regional Knowledge-Based Economy Index Using K-Means and Fuzzy C-Means Clustering Algorithms
The knowledge-based economy is the newest pattern of production in the current era. So far, this pattern has resulted in unique achievements for a wide range of countries. This study aims to classify the provinces of Iran in terms of Knowledge-based economy. The classification of provinces based on...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Tarbiat Modares University
2021-03-01
|
| Series: | پژوهشهای اقتصادی |
| Subjects: | |
| Online Access: | http://ecor.modares.ac.ir/article-18-45144-en.pdf |
| Summary: | The knowledge-based economy is the newest pattern of production in the current era. So far, this pattern has resulted in unique achievements for a wide range of countries. This study aims to classify the provinces of Iran in terms of Knowledge-based economy. The classification of provinces based on their similarity in achieving the knowledge-based production pattern is the first step for correct and realistic planning. The same version cannot be used for different provinces. The regional knowledge-based economy index is defined in three dimensions: education, innovation, and information and communication technology, based on 15 sub-indices. The classification is based on the clustering technique, which is one of the branches of unsupervised learning. To do this, k-means and fuzzy c-means algorithms are used simultaneously to compare their results. The optimal number of clusters is calculated through the Silhouette coefficient. This coefficient also indicates the accuracy of the clustering results. Clustering based on the fuzzy c-means algorithm in 6-cluster case with a Silhouette coefficient of 0.77 is the most appropriate classification for research purposes. The results show that there is a clear discrepancy between different provinces in the context of knowledge-based economy. Tehran and Alborz are in separate clusters and are among the leading classes compared to others, while more than half of the provinces belong to backward cluster. |
|---|---|
| ISSN: | 1735-6768 2980-7832 |