Research Article
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Year 2022, Volume 2, Issue 2, 19 - 27, 01.10.2022

Abstract

References

  • Julia, S., Oliver, S. (2016). Multi-objective three stage design optimization for island microgrids. Appl Energy 2016;165:789–800.
  • Zhang Z. et al. (2021). Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method. Energy Reports 7, 250–256
  • Garza-Ulloa, J. (2018). Chapter 6 - Application of mathematical models in biomechatronics: artificial intelligence and time-frequency analysis, Applied Biomechatronics using Mathematical Models.
  • Murtagh, F. Contreras, P., (2019). Algorithms for hierarchical clustering: an overview, II. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7 (6) (2017), p. e1219.
  • Ishizaka, A. (2021). A Stochastic Multi-criteria divisive hierarchical clustering algorithm. B. Lokman and M. Tasiou, Omega, vol. 103.
  • De Smet, Y. (2014). An extension of PROMETHEE to divisive hierarchical multicriteria clustering”, 2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEE, pp. 555-558.
  • D. Müllner, (2021). Modern hierarchical, agglomerative clustering algorithms. Comput. Sci., pp. 1-29.
  • Zhou, S., Xu, Z. Liu, F., (2017). Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Trans. Neural Networks Learn. Syst., 28 (12), pp. 3007-3017.
  • Zhu, E., Ma, R. (2018). An effective partitional clustering algorithm based on new clustering validity index. Applied Soft Computing 71, 608–621.
  • Zhu, S., Xu, L. (2018). Many-objective fuzzy centroids clustering algorithm for categorical data”, Expert Systems With Applications 96, 230–248.
  • Knuth, D., (1973). The Art of Computer Programming, Sorting and Searching. Vol. 3, Addison-Wesley, Massachusetts.
  • Jacques, J., Biernacki, C. (2020). Model-based clustering for rank data based on an insertion sorting algorithm. In: 17th Rencontres de la Société Francophone de Classification, La Réunion.
  • Zhan, K., Niu, C., Chen, C., Nie, F., Zhang, C., Yang, Y. (2018). “Graph structure fusion for multiview clustering”, IEEE Transactions on Knowledge and Data Engineering, 31 (10), pp. 1984-1993
  • Tang, C., Zhu, X., Liu, X., Li, M., Wang, P., Zhang, C., Wang, L. (2018). Learning a joint affinity graph for multiview subspace clustering”, IEEE Transactions on Multimedia, 21 (7), pp. 1724-1736.
  • Kumar, A., Iii, H.D. (2011). A co-training approach for multi-view spectral clustering”, International Conference on International Conference on Machine Learning, Omnipress, pp. 393-400.
  • Wang, K., Porter, M.D. (2018). Optimal Bayesian clustering using non-negative matrix factorization”, Computational Statistics and Data Analysis 128. 395–411
  • Randles, B. M., Pasquetto, I. V., Golshan, M. S., and Borgman, C. L.(2017). Using the jupyter notebook as a tool for open science: An empirical study,” in ACM/IEEE Joint Conference on Digital Libraries (JCDL).
  • McKinney, W. (2011). Pandas: A foundational python library for data analysis and statistics. Python for High Performance and Scientific Computing, vol. 14.
  • Greenfield, P., Miller, J. T., Hsu, J. & White, R. L (2003). Numarray: a new scientific array package for python. In PyCon DC.
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, vol. 12, , pp. 2825-2830.

The Comparative Effects of Clustering Algorithms on CPU and GPU

Year 2022, Volume 2, Issue 2, 19 - 27, 01.10.2022

Abstract

The algorithm clustering can be defined as the operation of separating the populace or pieces of information into various groups. This article aims to construct a performance comparison for Partitional Clustering by using random, k-means++ algorithms implemented with Scikit-Learn and k-means++, Tunnel k-means algorithms implemented with TensorFlow-GPU by means of their execution times. As a final output, a related comparison table will be printed by supplying their framework specifications. Since the article does not focus on the context of data, the necessary data sets will be produced in a random manner.

References

  • Julia, S., Oliver, S. (2016). Multi-objective three stage design optimization for island microgrids. Appl Energy 2016;165:789–800.
  • Zhang Z. et al. (2021). Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method. Energy Reports 7, 250–256
  • Garza-Ulloa, J. (2018). Chapter 6 - Application of mathematical models in biomechatronics: artificial intelligence and time-frequency analysis, Applied Biomechatronics using Mathematical Models.
  • Murtagh, F. Contreras, P., (2019). Algorithms for hierarchical clustering: an overview, II. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7 (6) (2017), p. e1219.
  • Ishizaka, A. (2021). A Stochastic Multi-criteria divisive hierarchical clustering algorithm. B. Lokman and M. Tasiou, Omega, vol. 103.
  • De Smet, Y. (2014). An extension of PROMETHEE to divisive hierarchical multicriteria clustering”, 2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEE, pp. 555-558.
  • D. Müllner, (2021). Modern hierarchical, agglomerative clustering algorithms. Comput. Sci., pp. 1-29.
  • Zhou, S., Xu, Z. Liu, F., (2017). Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Trans. Neural Networks Learn. Syst., 28 (12), pp. 3007-3017.
  • Zhu, E., Ma, R. (2018). An effective partitional clustering algorithm based on new clustering validity index. Applied Soft Computing 71, 608–621.
  • Zhu, S., Xu, L. (2018). Many-objective fuzzy centroids clustering algorithm for categorical data”, Expert Systems With Applications 96, 230–248.
  • Knuth, D., (1973). The Art of Computer Programming, Sorting and Searching. Vol. 3, Addison-Wesley, Massachusetts.
  • Jacques, J., Biernacki, C. (2020). Model-based clustering for rank data based on an insertion sorting algorithm. In: 17th Rencontres de la Société Francophone de Classification, La Réunion.
  • Zhan, K., Niu, C., Chen, C., Nie, F., Zhang, C., Yang, Y. (2018). “Graph structure fusion for multiview clustering”, IEEE Transactions on Knowledge and Data Engineering, 31 (10), pp. 1984-1993
  • Tang, C., Zhu, X., Liu, X., Li, M., Wang, P., Zhang, C., Wang, L. (2018). Learning a joint affinity graph for multiview subspace clustering”, IEEE Transactions on Multimedia, 21 (7), pp. 1724-1736.
  • Kumar, A., Iii, H.D. (2011). A co-training approach for multi-view spectral clustering”, International Conference on International Conference on Machine Learning, Omnipress, pp. 393-400.
  • Wang, K., Porter, M.D. (2018). Optimal Bayesian clustering using non-negative matrix factorization”, Computational Statistics and Data Analysis 128. 395–411
  • Randles, B. M., Pasquetto, I. V., Golshan, M. S., and Borgman, C. L.(2017). Using the jupyter notebook as a tool for open science: An empirical study,” in ACM/IEEE Joint Conference on Digital Libraries (JCDL).
  • McKinney, W. (2011). Pandas: A foundational python library for data analysis and statistics. Python for High Performance and Scientific Computing, vol. 14.
  • Greenfield, P., Miller, J. T., Hsu, J. & White, R. L (2003). Numarray: a new scientific array package for python. In PyCon DC.
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, vol. 12, , pp. 2825-2830.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Pınar ERSOY> (Primary Author)
BOGAZICI UNIVERSITY
0000-0001-9591-3037
Türkiye


Mustafa ERŞAHİN>
DOKUZ EYLUL UNIVERSITY
0000-0003-4318-8288
Türkiye


Buket ERŞAHİN>
İZMİR YÜKSEK TEKNOLOJİ ENSTİTÜSÜ
0000-0002-1726-8164
Türkiye

Publication Date October 1, 2022
Published in Issue Year 2022, Volume 2, Issue 2

Cite

APA Ersoy, P. , Erşahin, M. & Erşahin, B. (2022). The Comparative Effects of Clustering Algorithms on CPU and GPU . Artificial Intelligence Theory and Applications , 2 (2) , 19-27 . Retrieved from https://dergipark.org.tr/en/pub/aita/issue/72862/1141500