Öz
Özet
Spektral kümeleme hem normalize hem de normalize edilmemiş yöntemler için geliştirilmiştir. Bununla birlikte,
iki yöntem arasında seçim yapmak henüz GUI’de (Grafik Kullanıcı Arayüzü) kurulmamıştır. Bu yazıda, GUI-MATLAB
kullanarak farklı kümeleme algoritmaları uyguluyoruz, daha sonra bu üç yöntemle kümeleme, benzer verikümeleri çiftleri için karşılaştırılıyor. Modelimiz, spektral, hiyerarşik ve yoğunluk temelli yöntemler gibi üç farklı kümeleme
yöntemini kullanmaktadır, daha sonra kümeleme için farklı geometrik, çok aralıklı ve çok düzeyli benzer
veri kümeleri grafikler kullanmaktadır. Sonuç olarak, yukarıdaki üç kümeleme algoritması, (geometrik, çok menzilli
ve çok seviyeli) farklı ortamlar için denenmiştir. Benzetim sonucu, bu çift geometrik veri kümelerinin kümelenmesini
göstermektedir: Eş merkezli daireler, yarı daireler ve toplama. Buna göre, spektral algoritma, veri kümeleri
arasında 2000’den fazla çift nokta ve 500’den fazla veri kümesindeki üstün kümeleme özelliklerine sahiptir.
Anahtar Kelimeler
Sınıflandırma K-ortalama Spektral metod Laplace Özdeğer vektör GUI
Kaynakça
- S. Sharma, “Applied Multivariate Techniques,” John Willey Sons, 1996.
- [H. Tatlıdil, “Uygulamalı çok değişkenli istatistiksel analiz,” Akad. Yayınları, 1996.
- Q. Li, Y. Ren, L. Li, and W. Liu, “Fuzzy based af fi nity learning for spectral clustering,” Pattern Recognit., vol. 60, pp. 531–542, 2016.
- I. B. Society, “A General Coefficient of Similarity and Some of Its Properties Author ( s ): J . C . Gower Published by : International Biometric Society Stable URL : http://www.jstor.org/stable/2528823 International Biometric Society is collaborating with JSTOR to digitize , preserve and extend,” vol. 27, no. 4, pp. 857–871, 2018.
- F. H. C. Guttiérrez Toscano, P.,& Marriott, “Unsupervised classification of chemical compounds. Journal of the Royal Statistical Society,” Ser. C (Applied Stat., vol. 48, no. 2, pp. 153–163, 1999.
- C. Hair Jr, J. F., Anderson, R. E., Tatham, R. L., & William, “Multivariate data analysis with readings.,” New Jersy Prentice Hall., 1995.
- M. R. Anderberg, “Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks.,” Acad. Press, vol. 19, 2014.
- M. S. Blashfield, R. K.,& Aldenderfer, “The literature on cluster analysis. Multivariate Behavioral Research,” vol. 13, no. 3, pp. 271–295, 1978.
- S. H. Spielmat, D. A.,& Teng, “Spectral partitioning works: Planar graphs and finite element meshes.,” Found. Comput. Sci. Proceedings., 37th Annu. Symp. IEEE., pp. 96–105, 1996.
- M. Everitt, B. S., Landau, S., & Leese, “Clustering analysis,” Arnold, London, 2001.
- M. Han, J., Pei, J., & Kamber, Data mining: concepts and techniques. Elsevier, 2011.
- A. Ş. Çelik, M., Dadaşer-Çelik, F., & Dokuz, “Anomaly detection in temperature data using dbscan algorithm.,” Innov. Intell. Syst. Appl. (INISTA), 2011 Int. Symp. IEEE, pp. 91–95.
- M. Ester, H. Kriegel, X. Xu, and D.- Miinchen, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” 1996.
- H. (Eds. ). Ho, T. B., Cheung, D., & Liu, “Advances in Knowledge Discovery and Data Mining,” in 9th Pacific- Asia Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005, Proceedings(Vol. 3518). Springer., 2005.
- G. Yazgan, E.,& KAYAALP, “Kümeleme (Cluster) Analizi Yöntemlerinin Karşılaştırmalı Olarak İncelenmesi ve Tarımsal Araştırmalarda Kullanılması.,” Zootekni Anabilim Dalı, Adana., 2002.
- R. Atkinson, Q., Nicholls, G., Welch, D., & Gray, “From words to dates: water into wine, mathemagic or phylogenetic inference?,” Trans. Philol. Soc., vol. 103, no. 2, pp. 193–219, 2005.
- A. Kannan, R., Vempala, S., & Vetta, “On clusterings: Good, bad and spectral,” J. ACM (JACM), 51(3), 497- 515., 2004.
- F. H. C. Marriott, “Practical Problems in a Method of Cluster Analysis,” Biometrics, vol. 27, no. 3, pp. 501– 514, 1971.
- https://www.mathworks.com/matlabcentral/fileexchange/34412-fast-and-efficient-spectral-clustering
Implementation of Different Clustering Algorithms/Farklı Sınıflandırma Algoritmalarının Uygulamaları
Öz
Abstract
Spectral clustering is developed for both normalized and unnormalized methods. However, selecting between the
two methods is not established in the GUI (Graphical User Interface) yet . In this paper , we implement different
clustering algorithms using GUI-MATLAB, then, the clustering by these three methods, is compared for similar pairs
of datasets. Our model is employing such three different clustering methods which are spectral, hierarchical
and density based methods, then employing different geometrical, multi-range, and multi-level similar datasets
pairs of graph for clustering. As result, the above three clustering algorithms are experimented for different environments
which are (geometrical, multi-range and multi-level). The simulation result shows the clustering of
these pairs of geometrical datasets which are: Concentric circles, Semi-circles, and Aggregation. Accordingly, the
spectral algorithm has superior clustering in case of big datasets more than 2000 pairs points and range more
than 500 levels among datasets
Anahtar Kelimeler
Kaynakça
- S. Sharma, “Applied Multivariate Techniques,” John Willey Sons, 1996.
- [H. Tatlıdil, “Uygulamalı çok değişkenli istatistiksel analiz,” Akad. Yayınları, 1996.
- Q. Li, Y. Ren, L. Li, and W. Liu, “Fuzzy based af fi nity learning for spectral clustering,” Pattern Recognit., vol. 60, pp. 531–542, 2016.
- I. B. Society, “A General Coefficient of Similarity and Some of Its Properties Author ( s ): J . C . Gower Published by : International Biometric Society Stable URL : http://www.jstor.org/stable/2528823 International Biometric Society is collaborating with JSTOR to digitize , preserve and extend,” vol. 27, no. 4, pp. 857–871, 2018.
- F. H. C. Guttiérrez Toscano, P.,& Marriott, “Unsupervised classification of chemical compounds. Journal of the Royal Statistical Society,” Ser. C (Applied Stat., vol. 48, no. 2, pp. 153–163, 1999.
- C. Hair Jr, J. F., Anderson, R. E., Tatham, R. L., & William, “Multivariate data analysis with readings.,” New Jersy Prentice Hall., 1995.
- M. R. Anderberg, “Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks.,” Acad. Press, vol. 19, 2014.
- M. S. Blashfield, R. K.,& Aldenderfer, “The literature on cluster analysis. Multivariate Behavioral Research,” vol. 13, no. 3, pp. 271–295, 1978.
- S. H. Spielmat, D. A.,& Teng, “Spectral partitioning works: Planar graphs and finite element meshes.,” Found. Comput. Sci. Proceedings., 37th Annu. Symp. IEEE., pp. 96–105, 1996.
- M. Everitt, B. S., Landau, S., & Leese, “Clustering analysis,” Arnold, London, 2001.
- M. Han, J., Pei, J., & Kamber, Data mining: concepts and techniques. Elsevier, 2011.
- A. Ş. Çelik, M., Dadaşer-Çelik, F., & Dokuz, “Anomaly detection in temperature data using dbscan algorithm.,” Innov. Intell. Syst. Appl. (INISTA), 2011 Int. Symp. IEEE, pp. 91–95.
- M. Ester, H. Kriegel, X. Xu, and D.- Miinchen, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” 1996.
- H. (Eds. ). Ho, T. B., Cheung, D., & Liu, “Advances in Knowledge Discovery and Data Mining,” in 9th Pacific- Asia Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005, Proceedings(Vol. 3518). Springer., 2005.
- G. Yazgan, E.,& KAYAALP, “Kümeleme (Cluster) Analizi Yöntemlerinin Karşılaştırmalı Olarak İncelenmesi ve Tarımsal Araştırmalarda Kullanılması.,” Zootekni Anabilim Dalı, Adana., 2002.
- R. Atkinson, Q., Nicholls, G., Welch, D., & Gray, “From words to dates: water into wine, mathemagic or phylogenetic inference?,” Trans. Philol. Soc., vol. 103, no. 2, pp. 193–219, 2005.
- A. Kannan, R., Vempala, S., & Vetta, “On clusterings: Good, bad and spectral,” J. ACM (JACM), 51(3), 497- 515., 2004.
- F. H. C. Marriott, “Practical Problems in a Method of Cluster Analysis,” Biometrics, vol. 27, no. 3, pp. 501– 514, 1971.
- https://www.mathworks.com/matlabcentral/fileexchange/34412-fast-and-efficient-spectral-clustering
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Temmuz 2018 |
| Gönderilme Tarihi | 30 Haziran 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 2 Sayı: 1 |
