Innovation science and technologiy Volume 2 Issue 6 (2026) · pp. 438-444
INDUSTRIAL CLUSTER FORMATION AND ORGANIZATIONAL-ECONOMIC MECHANISMS: K-MEANS CLUSTERING ANALYSIS IN EMERGING ECONOMIES
Jabborov, Elbek
Abstract
This study applies the K-means clustering algorithm to identify and characterize industrial clusters in themanufacturing sector of Kashkadarya Province, Uzbekistan. Using comprehensive data from 234 manufacturing enterprisesduring the period 2022-2025, five distinct industrial clusters were identified: (1) heavy manufacturing (n=34), (2) chemical andpetrochemical industries (n=28), (3) machine-building and mechanical engineering (n=67), (4) light industries and processing(n=65), and (5) service-supporting activities (n=40). Silhouette coefficients exceeded 0.72 across all clusters, indicating robustclassification stability. Qualitative institutional analysis reveals significant differences in organizational mechanisms supportingcluster cohesion. Approximately 68 percent of clustered firms engage in regular inter-firm transactions, while 85 percent ofmachine-building firms participate in active informal information exchange networks. Formal institutions such as industryassociations were found in 78 percent of heavy manufacturing firms but only 34 percent of light industries. Educationalinstitutions and vocational centers significantly influence cluster location choices. The research identifies critical institutionaldeficiencies including inadequate quality standardization systems, limited research-industry collaboration mechanisms, andnascent venture capital availability. Findings provide empirical evidence for policymakers in developing targeted clusterdevelopment strategies in the Kashkadarya Province and similar regions in Central Asia.
industrial clustering, K-means algorithm, manufacturing sectors, Kashkadarya Province, cluster formation, organizational mechanisms, inter-firm networks, regional development, knowledge spillovers, institutional frameworks, emerging economies.
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