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Ketels, C. H. M. Recent research on competitiveness and clusters: what are the implications for regional policy? Camb. J. Reg. Econ. Soc. 6 (2), 269–284. https://doi.org/10.1093/cjres/rst008 (2013).

Google Scholar 

Smorodinskaya, N. V. & Katukov, D. D. When and why regional clusters become basic Building blocks of modern economy. Baltic Region. 11 (3), 61–91. https://doi.org/10.5922/2079-8555-2019-3-4 (2019).

Google Scholar 

Boronenko, V. & Zeibote, Z. The potential of cluster development and the role of cluster support policies in Latvia. Economic Annals. 56 (191), 35–67 (2011).

Google Scholar 

Hill, E. W. & Brennan, J. F. A methodology for identifying the drivers of industrial clusters: the foundation of regional competitive advantage. Econ. Dev. Q. 14 (1), 65–96. https://doi.org/10.1177/089124240001400109 (2000).

Google Scholar 

Pickernell, D., Rowe, P. A., Christie, M. J. & Brooksbank, D. Developing a framework for network and cluster identification for use in economic development policy-making. Entrepreneurship Reg. Dev. 19 (4), 339–358. https://doi.org/10.1080/08985620701275411 (2007).

Google Scholar 

Stejskal, J. Comparison of often applied methods for industrial cluster identification. In: International Conference on Development, Energy, Environment, Economics (DEEE), Puerto, De La Cruz, (pp 282–286). WSEAS Press. (2010).

Bergman, E. M. & Feser, E. J. Industrial and Regional Clusters: Concepts and Comparative applications. Web Book in Regional Science (West Virginia University, 1999). https://researchrepository.wvu.edu/rri-web-book/5/Regional Research Institute.

Terstriep, J. Cluster mapping: Analysis grid, Institute for Work and Technology. (2008). https://www.iat.eu/media/wp6-cluster_mapping_v1_5.pdf. Accessed 4 March 2025.

Hamoud, A. K., Abd Ulkareem, M., Hussain, H. N., Mohammed, Z. A. & Salih, G. M. Improve HR decision-making based on data mart and OLAP. Journal of Physics: Conference Series, 1530(1), 012058. (2020). https://doi.org/10.1088/1742-6596/1530/1/012058

Dahr, J. M., Hamoud, A. K., Najm, I. A. & Ahmed, M. I. Implementing sales decision support system using data Mart based on OLAP, KPI, and data mining approaches. J. Eng. Sci. Technol. 17 (1), 275–293 (2022).

Google Scholar 

Rino, R. Implementation of business intelligence in data superstore sales with online analytical processing method. bit-Tech 3 (2), 44–50. https://doi.org/10.32877/bt.v3i2.182 (2020).

Google Scholar 

Wardhani, F. Z. D. & Wiratama, J. Improving the quality of service: ETL implementation on data warehouse at pharmacy industry. Jurnal Tekno Kompak. 18 (1), 1–14. https://doi.org/10.33365/jtk.v18i1.3211 (2024).

Google Scholar 

Leung, C. K., Chen, Y., Hoi, C. S. H. & Shang, S. Machine learning and OLAP on big COVID-19 data. In Proceedings of the 2020 IEEE International Conference on Big Data (Big Data) (p. 9378407). IEEE. (2020). https://doi.org/10.1109/BigData50022.2020.9378407

Maliappis, M. T. & Kremmydas, D. An online analytical processing (OLAP) database for agricultural policy data: A Greek case study. Proceedings of the 7th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2015), Kavala, Greece, September 17–20, 2015, 214–225. CEUR-WS.org.pdf Accessed 16 Oct 2025 (2015).

Argüelles, M., Benavides, C. & Fernández, I. A new approach to the identification of regional clusters: hierarchical clustering on principal components. Appl. Econ. 46 (21), 2511–2519. https://doi.org/10.1080/00036846.2014.904491 (2014).

Google Scholar 

Guo, J., Lao, X. & Shen, T. Location-based method to identify industrial clusters in Beijing-Tianjin-Hebei area in China. J. Urban. Plan. Dev. 145 (1), 04019001. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000497 (2019).

Google Scholar 

Papagiannidis, S., See-To, E. W. K., Assimakopoulos, D. G. & Yang, Y. Identifying industrial clusters with a novel big-data methodology: are SIC codes (not) fit for purpose in the internet age? Comput. Oper. Res. 98, 355–366. https://doi.org/10.1016/j.cor.2017.06.010 (2018).

Google Scholar 

Porter, M. E. The competitive advantage of nations. Harvard Business Rev. 68 (2), 73–93 (1990).

Google Scholar 

Porter, M. E. Clusters and the new economics of competition. Harvard Business Rev. 76 (6), 77–90 (1998).

Google Scholar 

Ketels, C. H. M. The development of the cluster concept—present experiences and further developments [Paper presentation]. NRW Conference on Clusters, Duisburg, Germany. (2003).

Rosenfeld, S. A. Bringing business clusters into the mainstream of economic development. Eur. Plan. Stud. 5 (1), 3–23. https://doi.org/10.1080/09654319708720381 (1997).

Google Scholar 

Swann, G. M. P. & Prevezer, M. A comparison of the dynamics of industrial clustering in computing and biotechnology. Res. Policy. 25 (7), 1139–1157. https://doi.org/10.1016/S0048-7333(96)00897-9 (1996).

Google Scholar 

Martin, R. & Sunley, P. Deconstructing clusters: chaotic concept or policy panacea? J. Econ. Geogr. 3 (1), 5–35. https://doi.org/10.1093/jeg/3.1.5 (2003).

Google Scholar 

Malmberg, A. & Power, D. True clusters: A severe case of conceptual headache. In (eds Asheim, B., Cooke, P. & Martin, R.) Clusters and Regional Development: Critical Reflections and Explorations (50–68). Routledge. (2006).

Chain, C. P., Santos, A. C. D., Castro, L. G. D. & Prado, J. W. D. Bibliometric analysis of the quantitative methods applied to the measurement of industrial clusters. J. Economic Surv. 33 (1), 60–84. https://doi.org/10.1111/joes.12267 (2019).

Google Scholar 

Stek, P. E. Identifying Spatial technology clusters from Patenting concentrations using heat map kernel density Estimation. Scientometrics 126 (2), 911–930. https://doi.org/10.1007/s11192-020-03751-8 (2021).

Google Scholar 

Komorowski, M. Identifying industry clusters: A critical analysis of the most commonly used methods. Reg. Stud. Reg. Sci. 7 (1), 92–100. https://doi.org/10.1080/21681376.2020.1733436 (2020).

Google Scholar 

Sölvell, Ö., Ketels, C. & Lindqvist, G. The European cluster Observatory – EU cluster mapping and strengthening clusters in Europe. Cent. Strategy Competitiveness Stockholm School Econ. https://doi.org/10.2769/10419 (2009).

Google Scholar 

Sen, Ö. & Sandal, E. K. Industrial cluster analysis in Gaziantep Province using the three-star method. East. Geographical J. 22, 39–62 (2017).

Google Scholar 

Hollander, H. Methodology Report for the European Panorama of Clusters and Industrial Change and European Cluster Database (Publications Office of the European Union, 2020). https://doi.org/10.2826/466162

Ketels, Ketels, C. & Sölvell, Ö. Innovation Clusters in the 10 New Member States of the European Union (Publications Office of the European Union, 2007). https://doi.org/10.2769/10419

Sölvell, Ö., Ketels, C. & Lindqvist, G. Industrial specialization and regional clusters in the ten new EU member States. Competitiveness Review: Int. Bus. J. Incorporating J. Global Competitiveness. 18 (1/2), 104–130. https://doi.org/10.1108/10595420810874637 (2008).

Google Scholar 

Republic of Turkey Undersecretariat of Foreign Trade. Cluster mapping, analysis and cluster roadmaps – synthesis report (pp. 1–46). (2009). https://izmirkumelenme.weebly.com/ulusal-yay305nlar.html, Accessed 17 Oct 2025.

Manzini, R. B. & Luiz, D. S. C. Cluster identification: A joint application of industry concentration analysis and exploratory Spatial data analysis (ESDA). Competitiveness Review: Int. Bus. J. 29 (4), 401–415. https://doi.org/10.1108/CR-01-2018-0001 (2019).

Google Scholar 

Titova, N. Y., Pervuhin, M. A. & Baturin, G. G. Identification of regional clusters in the Russian Far East. Eur. Res. Stud. J. 20 (4A), 339–359. https://doi.org/10.35808/ersj/839 (2017).

Google Scholar 

Chen, J. & Jackson, R. Regional Industry Cluster Analysis for the Potomac Highlands in West Virginia (RRI Resource Document 2018-03) (Regional Research Institute, West Virginia University, 2018).

Meyer, D. F. & Niyimbanira, F. Formulation and application of a multi-variable location quotient index in the Mpumalanga Province, South Africa. Local Econ. 36 (4), 273–286. https://doi.org/10.1177/02690942211049505 (2021).

Google Scholar 

Campi, M., Dueñas, M. & Ciarli, T. Do creative industries enhance employment growth? Regional evidence from Colombia. Reg. Stud. 58 (3), 425–441. https://doi.org/10.1080/00343404.2023.2210620 (2024).

Google Scholar 

Khoirunnisa, I., Ratih, A., Ciptawaty, U., Wahyudi, H. & Murwiyati, A. Analysis of leading sectors supporting agriculture through the LQ and shift share approaches in Sumatera. Int. J. Educ. Social Stud. Manage. 4 (3), 932–949. https://doi.org/10.52121/ijessm.v4i3.420 (2024).

Google Scholar 

Çelik, E. & Sandal, E. K. The regional concentration structure of turkey’s manufacturing industry and its subsectors: A comparative location-quotient analysis for 2011–2020. Kahramanmaraş Sütçü İmam Univ. J. Social Sci. 19 (3), 1452–1468. https://doi.org/10.33437/ksusbd.1122318 (2022).

Google Scholar 

Urhan, F. B. & Sandal, E. K. The Spatial pattern of turkey’s textile industry: A comparative location-quotient analysis for 2009–2015. Int. J. Geogr. Geogr. Educ. 40, 172–189. https://doi.org/10.32003/iggei.537354 (2019).

Google Scholar 

Sandal, E. K. & Şen, Ö. The agglomerated industrial sectors in Gaziantep and these sector’s distribution in Turkey. Electron. Turkish Stud. 11 (8), 313–334. https://doi.org/10.7827/TurkishStudies.9612 (2016).

Google Scholar 

Yamaç, B. Cluster formation in the textile sector: the case of the Turkish textile industry. J. Econ. Administrative Sci. 9 (2), 215–232. https://doi.org/10.32003/iggei.537354 (2019).

Google Scholar 

Ögel, C. & Avcı, S. Analysis of gaziantep’s manufacturing industry using the concentration coefficient. Abant Social Sci. J. 23 (2), 1000–1016. https://doi.org/10.11616/asbi.1265986 (2023).

Google Scholar 

Seki, İ., Arslan, M. & Bektaş, S. Cluster analysis of the TR222 Çanakkale region. Int. J. Manage. Social Stud. 5 (10), 15–28 (2018).

Google Scholar 

Duran, E. Three-star analysis of the manufacturing sector in Çorum Province. East. Geographical Rev. 29 (51), 41–49. https://doi.org/10.17295/ataunidcd.1448869 (2024).

Google Scholar 

Yapraklı, S. & Aslan, Ö. F. Clustering potential and local competitiveness in the provinces of the TRA1 region (Erzurum, Erzincan, Bayburt): A field study based on three-star analysis. J. Econ. Administrative Sci. 24 (2), 429–458. https://doi.org/10.53443/anadoluibfd.1115658 (2023).

Google Scholar 

Karaçayır, E. Industrial clustering potential of the TR52 level 2 region. J. Political Sci. Fac. Necmettin Erbakan Univ. 6 (1), 230–243. https://doi.org/10.51124/jneusbf.2024.86 (2024).

Google Scholar 

Bayraktar, F. & Sekmen, F. Investigation of potential investment topics in İzmir Province. Turkish Development Bank, Istanbul. (2012). https://www.kalkinmakutuphanesi.gov.tr/dokuman/izmir-ili-potansiyel-yatirim-konulari-arastirmasi/614 Accessed 17 Oct 2025.

Gül, M., Topçuoğlu, E. M. & Çetinel, S. Three-star Cluster Analysis Study of the TR82 Region (North Anatolia Development Agency. Ankara, 2014).

Karagüney, F. Sectoral Competitiveness Analysis of the Manufacturing Industry in the Konya-Karaman Region (Mevlana Development Agency, 2019).

Central Anatolia Development Agency. Leading Sectors in the TR72 Region (Central Anatolia Development Agency Publications. Kayseri, 2021).

Er, F. Sectoral Competitiveness Analysis of the TR71 Region (Ahiler Development Agency. Nevşehir, 2022).

Çakay, M. E., Ertan, Y., Toprak, İ. & Özen, M. S. Current Status Analysis of the Industry in the TRB2 Region (Eastern Anatolia Development Agency.Van, 2014).

Eastern Anatolia Project Regional Development Administration. Entrepreneurship and Innovation Needs Analysis and Clustering Studies (Eastern Anatolia Project Regional Development Administration. Erzurum, 2017).

Demirdöğen, S. Identification of sectors with clustering potential: an application to the TRA1 level 2 region. J. Social Sci. Inst. Bolu Abant İzzet Baysal Univ. 18 (4), 85–113. https://doi.org/10.11616/asbed.v18i41997.505856 (2018).

Google Scholar 

Amin, M. M., Sutrisman, A. & Dwitayanti, Y. Development of star-schema model for lecturer performance in research activities. Int. J. Adv. Comput. Sci. Appl. 12 (9), 74–80. https://doi.org/10.14569/IJACSA.2021.0120909 (2021).

Google Scholar 

Cai, Y., Chen, J., Fan, X. & Yu, Z. Study on the regional economic data analysis and mining platform based on OLAM. In Proceedings of the Second International Workshop on Education Technology and Computer Science (pp. 550–554). IEEE, Wuhan, China (2010).

Toprak, A. & Çetinyokuş, T. Using online analytical processing (OLAP) tools in determining regional industrial clustering potential. J. Reg. Dev. 01 (03), 257–270. https://doi.org/10.61138/bolgeselkalkinmadergisi.1329622 (2023).

Google Scholar 

Markl, V., Ramsak, F. & Bayer, R. Improving OLAP performance by multidimensional hierarchical clustering. In Proceedings of the 1999 International Database Engineering and Applications Symposium (IDEAS ‘99) (pp. 165–177). IEEE (1999).

Ebrahimi, M. Knowledge extraction and analysis to evaluate the financial performance of an organization using OLAM. Int. J. Mod. Educ. Comput. Sci. 10 (12), 20–27. https://doi.org/10.5815/ijmecs.2018.12.03 (2018).

Google Scholar 

Song, Y. & Ge, C. Research on the integration architecture of OLAM and OLAP. In 2011 International Conference on Electric Information and Control Engineering (pp. 1656–1659). IEEE. (2011), April.

Saranya, C. & Manikandan, G. A study on normalization techniques for privacy preserving data mining. Int. J. Eng. Technol. (IJET). 5 (3), 2701–2704 (2013).

Google Scholar 

Oti, E. U., Olusola, M. O., Godwin, O. O. & Nwankwo, C. H. New k-means clustering method using minkowski’s distance as its metric. Inform. Technol. 4 (1), 28–41. https://doi.org/10.52589/BJCNIT-XEPSJBWX (2020).

Google Scholar 

Wong, M. A. Asymptotic properties of univariate sample k-means clusters. J. Classif. 1 (1), 255–270. https://doi.org/10.1007/BF01890126 (1984).

Google Scholar 

Qiu, D. & Tamhane, A. C. A comparative study of the K-means algorithm and the normal mixture model for clustering: univariate case. J. Stat. Plann. Inference. 137 (11), 3722–3740. https://doi.org/10.1016/j.jspi.2007.03.045 (2007).

Google Scholar 

Wang, B., Zhou, B., Zou, M., Liu, Q., Zhao, R., Dai, M., … Wang, Y. (2023, October).Research on condition monitoring of offshore wind turbine gearbox based on K-means clustering and extreme learning machine model. In 2023 International Conference on Artificial Intelligence and Power Engineering (AIPE) (pp. 71–76). IEEE.

Izmir Development Agency. Izmir clustering analysis. Izmir Development Agency. (2010). https://www.kalkinmakutuphanesi.gov.tr/dokuman/izmir-kumelenme-analizi/611 Accessed 16 Oct 2025.

South Marmara Development Agency. TR22 (Balıkesir, Çanakkale) South Marmara Region manufacturing industry strategy and action plan. South Marmara Development Agency. (2017). https://www.kalkinmakutuphanesi.gov.tr/dokuman/guney-marmara-bolgesi-imalat-sanayi-stratejisi-ve-eylem-plani/588 Accessed 16 Oct 2025.

Yavan, N. Entrepreneurship and innovation needs analysis of the KOP Region. Republic of Turkey Ministry of Industry and Technology, KOP Regional Development Administration. (2018). https://www.kalkinmakutuphanesi.gov.tr/assets/upload/dosyalar/girisimcilik-ve-yenilik-ihtiyac-analizi-sonuc-ve-degerlendirme-analizi-raporu.pdf Accessed 16 Oct 2025.

Crawley, A., Beynon, M. & Munday, M. Making location quotients more relevant as a policy aid in regional Spatial analysis. Urban Stud. 50 (9), 1854–1869. https://doi.org/10.1177/0042098012466601 (2013).

Google Scholar