SavedAdd your journal

Journals / Optimum Ekonomi ve Yönetim Bilimleri Dergisi / 2020 / Cilt: 7 - Sayı: 1

Clustering Based on Fuzzy Adaptive Particle Swarm Optimization Approach

Bulanık Parçacık Sürü Optimizasyon Yaklaşımı Temelli Kümeleme

Mehmet AKSARAYLI
Osman PALA
Pages
279–296
Publication Date
2020-01-15
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The process of grouping the observation points with the same characteristics in terms of the values of the characteristics is called clustering analysis.
📊
Key Findings & Data: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.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Parçacık Sürü Optimizasyonu · Kümeleme Analizi · Bulanık Adaptif

Abstract

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.

Özet

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.

🔖

Cite This Article

AKSARAYLI, M., PALA, O. (2020). Clustering Based on Fuzzy Adaptive Particle Swarm Optimization Approach. Optimum Ekonomi ve Yönetim Bilimleri Dergisi, Cilt: 7 - Sayı: 1, 279–296. https://ajindex.com/articles/34301

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. 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.
  2. 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.
  3. Armano, G. & Framani, M. R. (2016), Multiobjective Clustering Analysis Using Particle Swarm Optimization. Expert Systems With Applications, 55, 184–193.
  4. Belbin, L., & McDonald, C. (1993). Comparing three classification strategies for use in ecology. Journal of Vegetation Science, 4(3), 341-348.
  5. 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).
  6. Cura, T. (2012). A particle swarm optimization approach to clustering. Expert Systems with Applications, 39(1), 1582-1588.
  7. 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.
  8. 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).
  9. 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.
  10. Ghorpade, J. A., & Metre, V. A. (2014). Clustering Multidimensional Data with PSO based Algorithm. Soft Computing and Artificial Intelligence, 87(6), 1-7.
  11. 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.
  12. 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.
  13. Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 64-72.
  14. 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 optim