نویسندگان
Department of Computer Engineering, Yazd University, Yazd, Iran.
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.
کلیدواژهها [English]
[1] P. Saini, J. Kaur, and S. Lamba, “A Review on Pattern Recognition Using Machine Learning,” Lecture Notes in Mechanical Engineering, pp. 619–627, 2021.
[2] C. Li, F. Kulwa, J. Zhang, Z. Li, H. Xu, and X. Zhao, “A review of clustering methods in microorganism image analysis,” Advances in Intelligent Systems and Computing, vol. 1186, Springer, pp. 13–25, 2021.
[3] M. Subramaniam, A. Kathirvel, E. Sabitha, and H. A. Basha, “Modified firefly algorithm and fuzzy c-mean clustering based semantic information retrieval,” Journal of Web Engineering, vol. 20, no. 1, pp. 33–52, 2021.
[4] A. R., Sardar, and R. Havangi, “Performance improvement of automatic clustering algorithm of colored images through preprocessing using Self-Organizing Maps (SOM) neural network,” Tabriz Journal of Electrical Engineering, vol. 47, no. 3, pp. 1073-1082, 2017.
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[6] D. J. Bora and D. A. K. Gupta, “A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm,” International Journal of Computer Trends and Technology, vol. 10, no. 2, pp. 108–113, 2014.
[7] S, Rafiei, and P. Moradi, “Improving Performance of Fuzzy C-means Clustering Algorithm using Automatic Local Feature Weighting.,” Tabriz Journal of Electrical Engineering, vol. 46, no. 2, 2016.
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[11] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–296, 1967, Accessed: Apr. 29, 2021.
[12] Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., & Akinyelu, A. A. “A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects.” Engineering Applications of Artificial Intelligence, vol. 110, pp. 104743, 2022.
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[15] Ram A, Jalal S, Jalal A S, Kumar M. “A density based algorithm for discovering density varied clusters in large spatial databases,” International Journal of Computer Applications, vol. 3, no. 6, pp. 1-4, 2010.
[16] A. Smiti and Z. Elouedi, “DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques,” INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, pp. 573–578, 2012.
[17] C. Fraley and A. E. Raftery, “Model-based clustering, discriminant analysis, and density estimation,” Journal of the American Statistical Association, vol. 97, no. 458, pp. 611–631, 2002.
[18] C. Fraley, A. E. Raftery, T. B. Murphy, and L. Scrucca, “mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation.” Technical Report, Department of Statistics, University of Washington, no. 597, 2012.
[19] Jenni, V. R., Dua, A., Shobha, G., Shetty, J., & Dev, R. “Hybrid Density-based Adaptive Clustering using Gaussian Kernel and Grid Search,” Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp. 221-226, 2021.
[20] E. Güngör and A. Özmen, “Distance and density based clustering algorithm using Gaussian kernel,” Expert Systems with Applications, vol. 69, pp. 10–20, 2017.
[21] A. S. Shirkhorshidi, S. Aghabozorgi, and T. Ying Wah, “A Comparison study on similarity and dissimilarity measures in clustering continuous data,” PLoS One, vol. 10, no. 12, 2015.
[22] C. Luo, Y. Li, and S. M. Chung, “Text document clustering based on neighbors,” Data & Knowledge Engineering, vol. 68, no. 11, pp. 1271–1288, 2009.
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[27] J. C. Bezdek and N. R. Pal, “Some New Indexes of Cluster Validity,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, no. 3, pp. 301-315, 1998. Accessed: Apr. 29, 2021.
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[29] R. Kashef and M. S. Kamel, “Enhanced bisecting k-means clustering using intermediate cooperation,” Pattern Recognition, vol. 42, no. 11, pp. 2557-2569, 2009.
[30] J. Lipor and L. Balzano, “Clustering quality metrics for subspace clustering.” Pattern Recognition, vol. 104, pp. 107328, 2020.
[31] M. Gaurav and S. K. Mohanty, “A fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree,” Expert Systems with Applications, vol. 132, pp. 28-43, 2019.
[32] J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-Information-Based Registration of Medical Images: A Survey,” IEEE Transactions on Medical Imaging, vol. 22, no. 8, 2003.
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[1] P. Saini, J. Kaur, and S. Lamba, “A Review on Pattern Recognition Using Machine Learning,” Lecture Notes in Mechanical Engineering, pp. 619–627, 2021.
[2] C. Li, F. Kulwa, J. Zhang, Z. Li, H. Xu, and X. Zhao, “A review of clustering methods in microorganism image analysis,” Advances in Intelligent Systems and Computing, vol. 1186, Springer, pp. 13–25, 2021.
[3] M. Subramaniam, A. Kathirvel, E. Sabitha, and H. A. Basha, “Modified firefly algorithm and fuzzy c-mean clustering based semantic information retrieval,” Journal of Web Engineering, vol. 20, no. 1, pp. 33–52, 2021.
[4] A. R., Sardar, and R. Havangi, “Performance improvement of automatic clustering algorithm of colored images through preprocessing using Self-Organizing Maps (SOM) neural network,” Tabriz Journal of Electrical Engineering, vol. 47, no. 3, pp. 1073-1082, 2017.
[5] M. Kearns, Y. Mansour, and A. Y. Ng, “An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering,” Learning in Graphical Models, Springer Netherlands, pp. 495–520, 1998.
[6] D. J. Bora and D. A. K. Gupta, “A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm,” International Journal of Computer Trends and Technology, vol. 10, no. 2, pp. 108–113, 2014.
[7] S, Rafiei, and P. Moradi, “Improving Performance of Fuzzy C-means Clustering Algorithm using Automatic Local Feature Weighting.,” Tabriz Journal of Electrical Engineering, vol. 46, no. 2, 2016.
[8] Rasmussen and E. M, “Clustering algorithms.,” Information Retrieval: data Structures & algorithms, vol. 419, p. 442, 1992, Accessed: Apr. 29, 2021.
[9] S. Kaushik, D. Kundu, S. Ghosh, S. Das, and A. Abraham. “Data clustering using multi-objective differential evolution algorithms. .” Fundamenta Informaticae, vol. 97, no. 4, pp. 381-403, 2009.
[10] J. Ak and R. Dubes, “Algorithms for clustering data. Englewood Cliff,” NJ Prentice Hall, 1988, Accessed: Apr. 29, 2021.
[11] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–296, 1967, Accessed: Apr. 29, 2021.
[12] Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., & Akinyelu, A. A. “A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects.” Engineering Applications of Artificial Intelligence, vol. 110, pp. 104743, 2022.
[13] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231, 1996, Accessed: Apr. 29, 2021.
[14] M. Ankerst, M. M. Breunig, H. P. Kriegel, and J. Sander, “OPTICS: Ordering Points to Identify the Clustering Structure,” ACM Sigmod record, vol. 28, no. 2, pp. 49–60, 1999.
[15] Ram A, Jalal S, Jalal A S, Kumar M. “A density based algorithm for discovering density varied clusters in large spatial databases,” International Journal of Computer Applications, vol. 3, no. 6, pp. 1-4, 2010.
[16] A. Smiti and Z. Elouedi, “DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques,” INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, pp. 573–578, 2012.
[17] C. Fraley and A. E. Raftery, “Model-based clustering, discriminant analysis, and density estimation,” Journal of the American Statistical Association, vol. 97, no. 458, pp. 611–631, 2002.
[18] C. Fraley, A. E. Raftery, T. B. Murphy, and L. Scrucca, “mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation.” Technical Report, Department of Statistics, University of Washington, no. 597, 2012.
[19] Jenni, V. R., Dua, A., Shobha, G., Shetty, J., & Dev, R. “Hybrid Density-based Adaptive Clustering using Gaussian Kernel and Grid Search,” Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp. 221-226, 2021.
[20] E. Güngör and A. Özmen, “Distance and density based clustering algorithm using Gaussian kernel,” Expert Systems with Applications, vol. 69, pp. 10–20, 2017.
[21] A. S. Shirkhorshidi, S. Aghabozorgi, and T. Ying Wah, “A Comparison study on similarity and dissimilarity measures in clustering continuous data,” PLoS One, vol. 10, no. 12, 2015.
[22] C. Luo, Y. Li, and S. M. Chung, “Text document clustering based on neighbors,” Data & Knowledge Engineering, vol. 68, no. 11, pp. 1271–1288, 2009.
[23] K. Bache and M. Lichman, “UCI machine learning,”. https://ergodicity.net/2013/07/, accessed Apr. 29, 2021.
[24] “UCI machine learning repository,” Jul. 10, 2020. http://archive.ics.uci.edu/ml (accessed Apr. 29, 2021).
[25] A. K. Jain and M. H. C. Law, “Data Clustering : A User ’ s Dilemma,” pp. 1–10, 2005.
[26] S. Aghabozorgi, A. S. Shirkhorshidi, and T. Y. Wah, “Time-series clustering--a decade review,” Information Systems, vol. 53, pp. 16–38, 2015.
[27] J. C. Bezdek and N. R. Pal, “Some New Indexes of Cluster Validity,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, no. 3, pp. 301-315, 1998. Accessed: Apr. 29, 2021.
[28] H. Cui, M. Xie, Y. Cai, X. Huang, and Y. Liu, “Cluster validity index for adaptive clustering algorithms; Cluster validity index for adaptive clustering algorithms,” IET Communications, vol. 8, no. 13, pp. 2256–2263, 2013.
[29] R. Kashef and M. S. Kamel, “Enhanced bisecting k-means clustering using intermediate cooperation,” Pattern Recognition, vol. 42, no. 11, pp. 2557-2569, 2009.
[30] J. Lipor and L. Balzano, “Clustering quality metrics for subspace clustering.” Pattern Recognition, vol. 104, pp. 107328, 2020.
[31] M. Gaurav and S. K. Mohanty, “A fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree,” Expert Systems with Applications, vol. 132, pp. 28-43, 2019.
[32] J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-Information-Based Registration of Medical Images: A Survey,” IEEE Transactions on Medical Imaging, vol. 22, no. 8, 2003.
[33] W. M. Rand, “Objective criteria for the evaluation of clustering methods,” Journal of the American Statistical Association, vol. 66, no. 336, pp. 846–850, 1971.
[34] J. C. Rojas-Thomas, M. Santos, and M. Mora, “New internal index for clustering validation based
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