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Clustering Based on Fuzzy Adaptive Particle Swarm Optimization Approach

Yıl 2020, Cilt: 7 Sayı: 1, 279 - 296, 15.01.2020
https://doi.org/10.17541/optimum.575499

Öz

The process of grouping the observation points with the same characteristics in terms of the values of the characteristics is called clustering analysis. The aim of clustering, which is one of the techniques of machine learning, is to provide the implementation of different strategies for observation points in the different group by grouping the observation points. In clustering used in many disciplines, the process becomes difficult when there is no preliminary information such as the number of clusters. In cases where the number of sets is not predetermined, heuristic algorithms can be used, which can determine the number of sets according to the fitness function. In this study, in order to evaluate the success of the proposed Fuzzy Particle Swarm Optimization heuristic algorithm and fitness function in clustering, data sets frequently used in clustering analysis were utilized. According to the results of the analysis, it was observed that the proposed algorithm indicated a quite successful performance in finding the correct number of clusters and in the correct grouping of observations.

Kaynakça

  • Aladağ, C. H., Yolcu, U., Egrioğlu, E., ve Dalar, A. Z. (2012). A new time invariant fuzzy time series forecasting method based on particle swarm optimization. Applied Soft Computing, 12(10), 3291-3299.
  • Alswaitti, M., Albughdadi, M. & Mat Isa, N. A. (2018). Density-Based Particle Swarm Optimization Algorithm For Data Clustering. Expert Systems With Applications, 91: 170-186.
  • Armano, G. & Framani, M. R. (2016), Multiobjective Clustering Analysis Using Particle Swarm Optimization. Expert Systems With Applications, 55, 184–193.
  • Belbin, L., & McDonald, C. (1993). Comparing three classification strategies for use in ecology. Journal of Vegetation Science, 4(3), 341-348.
  • Chen, C.-Y., & Ye, F. (2004). Particle swarm optimization algorithm and its application to clustering analysis. In Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan (pp. 789–794).
  • Cura, T. (2012). A particle swarm optimization approach to clustering. Expert Systems with Applications, 39(1), 1582-1588.
  • Das, S., Abraham, A., & Konar, A. (2008). Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern recognition letters, 29(5), 688-699.
  • Eberhart, R. & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
  • Esmin, A. A., Coelho, R. A., & Matwin, S. (2015). A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artificial Intelligence Review, 44(1), 23-45.
  • Ghorpade, J. A., & Metre, V. A. (2014). Clustering Multidimensional Data with PSO based Algorithm. Soft Computing and Artificial Intelligence, 87(6), 1-7. Haldar, P., Pavord, I. D., Shaw, D. E., Berry, M. A., Thomas, M., Brightling, C. E., ... & Green, R. H. (2008). Cluster analysis and clinical asthma phenotypes. American journal of respiratory and critical care medicine, 178(3), 218-224.
  • Ketchen, D. J., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic management journal, 17(6), 441-458.
  • Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 64-72.
  • Ling, S. H., Chan, K. Y., Leung, F. H. F., Jiang, F., & Nguyen, H. (2016). Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization. Engineering Applications of Artificial Intelligence, 47, 68-80.
  • Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., & Valdez, M. (2013). Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Systems with Applications, 40(8), 3196-3206.
  • Niknam, T. (2010). A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem. Applied Energy, 87(1), 327-339.
  • Omran, M. G., Salman, A., & Engelbrecht, A. P. (2006). Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Analysis and Applications, 8(4), 332- 344.
  • Ortakçı, Y. ve Göloğlu, C. (2012). Parçacık Sürü Optimizasyonu İle Küme Sayısının Belirlenmesi. Akademik Bilişim Akademik Bilişim’12 - XIV. Akademik Bilişim Konferansı Bildirileri 1 - 3 Şubat 2012 Uşak Üniversitesi, 335–341.
  • Özmen, M., Delice, Y., ve Aydoğan, E. K. (2018). Telekomünikasyon Sektöründe PSO ile Müşteri Bölümlenmesi. Bilişim Teknolojileri Dergisi, 11(2), 163-173.
  • Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of marketing research, 20(2), 134-148.
  • Shi, Y., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on (Vol. 3, pp. 1945-1950). IEEE.
  • Shi, Y., & Eberhart, R. C. (2001). Fuzzy adaptive particle swarm optimization. In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on (Vol. 1, pp. 101-106). IEEE.
  • Shirkhorshidi, A. S., Aghabozorgi, S., & Wah, T. Y. (2015). A comparison study on similarity and dissimilarity measures in clustering continuous data. PloS one, 10(12), e0144059. 1-20.
  • Van der Merwe, D. W., & Engelbrecht, A. P. (2003, December). Data clustering using particle swarm optimization. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 1, pp. 215-220). IEEE.
  • Wolfson, M., Madjd-Sadjadi, Z., & James, P. (2004). Identifying national types: A cluster analysis of politics, economics, and conflict. Journal of Peace Research, 41(5), 607-623.
  • Yeh, J. H., Joung, F. J., ve Lin, J. C. (2014). CDV index: a validity index for better clustering quality measurement. Journal of Computer and Communications, 2(04), 163-171.
  • Zhao, Q., Xu, M., & Fränti, P. (2009, April). Sum-of-squares based cluster validity index and significance analysis. In International Conference on Adaptive and Natural Computing Algorithms (pp. 313-322). Springer, Berlin, Heidelberg.

Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme

Yıl 2020, Cilt: 7 Sayı: 1, 279 - 296, 15.01.2020
https://doi.org/10.17541/optimum.575499

Öz

Aynı özelliklere sahip gözlem noktalarını, özelliklerinin aldığı değerler açısından gruplara ayırma işlemine kümeleme analizi adı verilmektedir. Makine öğrenmesi tekniklerinden olan kümelemede amaç, gözlem noktalarını gruplayarak farklı gruptaki gözlem noktaları için farklı stratejiler uygulanmasının sağlanmasıdır. Birçok bilim dalında kullanılan kümelemede, küme sayısı gibi ön bilgilerin bulunmadığı durumlarda işlem zorlaşmaktadır. Küme sayısı önceden belirli olmadığı durumlarda, uygunluk fonksiyonuna göre küme sayısını belirleyebilen sezgisel algoritmalar kullanılabilmektedir. Çalışmada, önerilen Bulanık Parçacık Sürü Optimizasyonu sezgisel algoritmasının ve uygunluk fonksiyonunun kümelemedeki başarısını değerlendirebilmek adına kümeleme analizinde sıklıkla kullanılan veri setlerinden faydalanılmıştır. Analiz sonuçlarına göre önerilen algoritmanın doğru küme sayısını bulmada ve gözlemleri doğru gruplamada klasik yaklaşıma göre daha yüksek başarım gösterdiği gözlenmiştir.

Kaynakça

  • Aladağ, C. H., Yolcu, U., Egrioğlu, E., ve Dalar, A. Z. (2012). A new time invariant fuzzy time series forecasting method based on particle swarm optimization. Applied Soft Computing, 12(10), 3291-3299.
  • Alswaitti, M., Albughdadi, M. & Mat Isa, N. A. (2018). Density-Based Particle Swarm Optimization Algorithm For Data Clustering. Expert Systems With Applications, 91: 170-186.
  • Armano, G. & Framani, M. R. (2016), Multiobjective Clustering Analysis Using Particle Swarm Optimization. Expert Systems With Applications, 55, 184–193.
  • Belbin, L., & McDonald, C. (1993). Comparing three classification strategies for use in ecology. Journal of Vegetation Science, 4(3), 341-348.
  • Chen, C.-Y., & Ye, F. (2004). Particle swarm optimization algorithm and its application to clustering analysis. In Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan (pp. 789–794).
  • Cura, T. (2012). A particle swarm optimization approach to clustering. Expert Systems with Applications, 39(1), 1582-1588.
  • Das, S., Abraham, A., & Konar, A. (2008). Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern recognition letters, 29(5), 688-699.
  • Eberhart, R. & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
  • Esmin, A. A., Coelho, R. A., & Matwin, S. (2015). A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artificial Intelligence Review, 44(1), 23-45.
  • Ghorpade, J. A., & Metre, V. A. (2014). Clustering Multidimensional Data with PSO based Algorithm. Soft Computing and Artificial Intelligence, 87(6), 1-7. Haldar, P., Pavord, I. D., Shaw, D. E., Berry, M. A., Thomas, M., Brightling, C. E., ... & Green, R. H. (2008). Cluster analysis and clinical asthma phenotypes. American journal of respiratory and critical care medicine, 178(3), 218-224.
  • Ketchen, D. J., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic management journal, 17(6), 441-458.
  • Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 64-72.
  • Ling, S. H., Chan, K. Y., Leung, F. H. F., Jiang, F., & Nguyen, H. (2016). Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization. Engineering Applications of Artificial Intelligence, 47, 68-80.
  • Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., & Valdez, M. (2013). Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Systems with Applications, 40(8), 3196-3206.
  • Niknam, T. (2010). A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem. Applied Energy, 87(1), 327-339.
  • Omran, M. G., Salman, A., & Engelbrecht, A. P. (2006). Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Analysis and Applications, 8(4), 332- 344.
  • Ortakçı, Y. ve Göloğlu, C. (2012). Parçacık Sürü Optimizasyonu İle Küme Sayısının Belirlenmesi. Akademik Bilişim Akademik Bilişim’12 - XIV. Akademik Bilişim Konferansı Bildirileri 1 - 3 Şubat 2012 Uşak Üniversitesi, 335–341.
  • Özmen, M., Delice, Y., ve Aydoğan, E. K. (2018). Telekomünikasyon Sektöründe PSO ile Müşteri Bölümlenmesi. Bilişim Teknolojileri Dergisi, 11(2), 163-173.
  • Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of marketing research, 20(2), 134-148.
  • Shi, Y., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on (Vol. 3, pp. 1945-1950). IEEE.
  • Shi, Y., & Eberhart, R. C. (2001). Fuzzy adaptive particle swarm optimization. In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on (Vol. 1, pp. 101-106). IEEE.
  • Shirkhorshidi, A. S., Aghabozorgi, S., & Wah, T. Y. (2015). A comparison study on similarity and dissimilarity measures in clustering continuous data. PloS one, 10(12), e0144059. 1-20.
  • Van der Merwe, D. W., & Engelbrecht, A. P. (2003, December). Data clustering using particle swarm optimization. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 1, pp. 215-220). IEEE.
  • Wolfson, M., Madjd-Sadjadi, Z., & James, P. (2004). Identifying national types: A cluster analysis of politics, economics, and conflict. Journal of Peace Research, 41(5), 607-623.
  • Yeh, J. H., Joung, F. J., ve Lin, J. C. (2014). CDV index: a validity index for better clustering quality measurement. Journal of Computer and Communications, 2(04), 163-171.
  • Zhao, Q., Xu, M., & Fränti, P. (2009, April). Sum-of-squares based cluster validity index and significance analysis. In International Conference on Adaptive and Natural Computing Algorithms (pp. 313-322). Springer, Berlin, Heidelberg.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Makaleler
Yazarlar

Mehmet Aksaraylı 0000-0003-1590-4582

Osman Pala 0000-0002-2634-2653

Yayımlanma Tarihi 15 Ocak 2020
Gönderilme Tarihi 11 Haziran 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 7 Sayı: 1

Kaynak Göster

APA Aksaraylı, M., & Pala, O. (2020). Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme. Optimum Ekonomi Ve Yönetim Bilimleri Dergisi, 7(1), 279-296. https://doi.org/10.17541/optimum.575499
AMA Aksaraylı M, Pala O. Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme. OEYBD. Ocak 2020;7(1):279-296. doi:10.17541/optimum.575499
Chicago Aksaraylı, Mehmet, ve Osman Pala. “Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme”. Optimum Ekonomi Ve Yönetim Bilimleri Dergisi 7, sy. 1 (Ocak 2020): 279-96. https://doi.org/10.17541/optimum.575499.
EndNote Aksaraylı M, Pala O (01 Ocak 2020) Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme. Optimum Ekonomi ve Yönetim Bilimleri Dergisi 7 1 279–296.
IEEE M. Aksaraylı ve O. Pala, “Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme”, OEYBD, c. 7, sy. 1, ss. 279–296, 2020, doi: 10.17541/optimum.575499.
ISNAD Aksaraylı, Mehmet - Pala, Osman. “Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme”. Optimum Ekonomi ve Yönetim Bilimleri Dergisi 7/1 (Ocak 2020), 279-296. https://doi.org/10.17541/optimum.575499.
JAMA Aksaraylı M, Pala O. Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme. OEYBD. 2020;7:279–296.
MLA Aksaraylı, Mehmet ve Osman Pala. “Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme”. Optimum Ekonomi Ve Yönetim Bilimleri Dergisi, c. 7, sy. 1, 2020, ss. 279-96, doi:10.17541/optimum.575499.
Vancouver Aksaraylı M, Pala O. Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme. OEYBD. 2020;7(1):279-96.

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