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Abstract

Small-to-medium enterprises (SMEs) in regional (non-metropolitan) areas are considered when economic planning may require large data sets and sophisticated clustering techniques. The economic growth of regional areas was investigated using four clustering algorithms. Empirical analysis demonstrated that the modified global k-means algorithm outperformed other algorithms.

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