Cite

[1] Ankerst M., Breunig M, Kriegel H.P, Sandler J.: OPTICS: Ordering Points to Identify the Clustering Structure. Proceedings of the Int. Conf. on Management of Data, pp.49-60, (1999).10.1145/304181.304187Search in Google Scholar

[2] Babu G.P., Murty M.N.: Simulated annealing for selecting optimal initial seeds in the k-means algorithm. Indian Journal of Pure and Applied Mathematics, Vol 25, pp.85-94 (1994).Search in Google Scholar

[3] Bradley P., Fayyad U.: Refining initial points for k-means clustering. In Proceedings of the fifteenth international conference on knowledge discovery and data mining, New York, AAAI Press, pp. 9-15 (1998).Search in Google Scholar

[4] Chen X., Liu W., Qui H, Lai J: APSCAN: A parameter free algorithm for clustering. Pattern Recognition Letters, Vol. 32, pp.973-986 (2011).10.1016/j.patrec.2011.02.001Search in Google Scholar

[5] Chen J.: Hybrid clustering algorithm based on pso with the multidimensional asynchronism and stochastic disturbance method. Journal of Theoretical and Applied Information Technology, Vol.46, pp.434-440 (2012).Search in Google Scholar

[6] Chen Y., Tang S., Bouguila N., Wang C., Du J., Li H.: A Fast Clustering Algorithm based on pruning unnecessary distance computations in DBSCAN for High-Dimensional Data. Pattern Recognition Vol.83, pp.375-387 (2018)10.1016/j.patcog.2018.05.030Search in Google Scholar

[7] Darong H., Peng W.: Grid-based dbscan algorithm with referential parameters. Physics Procedia, Vol.24, Part B, pp.1166-1170 (2012).10.1016/j.phpro.2012.02.174Search in Google Scholar

[8] Ester M., Kriegel H.P, Sander J., Xu X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceeding of 2nd International Conference on Knowledge Discovery and Data Mining, pp.226-231 (1996).Search in Google Scholar

[9] Fränti P., Rezaei M., Zhao Q.: Centroid index: Cluster level similarity measure. Pattern Recognition, Vol.47, Issue 9, pp.3034-3045 (2014).10.1016/j.patcog.2014.03.017Search in Google Scholar

[10] Gabryel M.: The Bag-of-Words Method with Different Types of Image Features and Dictionary Analysis. Journal of Universal Computer Science 24(4), pp.357-371 (2018).Search in Google Scholar

[11] Gabryel M.: Data Analysis Algorithm for Click Fraud Recognition. Communications in Computer and Information Science, Vol.920, pp.437-446 (2018).10.1007/978-3-319-99972-2_36Search in Google Scholar

[12] Gabryel M., Damaševičius R., Przybyszewski K.: Application of the Bag-of-Words Algorithm in Classification the Quality of Sales Leads. Lecture Notes in Computer Science, Vol. 10841, pp.615-622 (2018).10.1007/978-3-319-91253-0_57Search in Google Scholar

[13] Hruschka E.R., de Castro L.N., Campello R.J.: Evolutionary algorithms for clustering gene-expression data, In: Data Mining, 2004. ICDM’04. Fourth IEEE International Conference on Data Mining, pp.403-406, IEEE (2004).Search in Google Scholar

[14] Jain A.K., Murty M.N, Flynn P.J: Data Clustering: A Review. ACM Computing Surveys, Vol.31, No.3, pp.264-323 (1999).10.1145/331499.331504Search in Google Scholar

[15] Karami A., Johansson R.: Choosing DBSCAN Parameters Automatically using Differential Evolution. International Journal of Computer Applications, Vol.91, pp.1-11 (2014).10.5120/15890-5059Search in Google Scholar

[16] Lai W., Zhou M., Hu F., Bian K., Song Q.: A New DBSCAN Parameters Determination Method Based on Improved MVO. IEEE Access, Vol.7 (2019).10.1109/ACCESS.2019.2931334Search in Google Scholar

[17] Liu Z., Zhou D., Wu N.: Varied Density Based Spatial Clustering of Application with Noise. In proceedings of IEEE Conference ICSSSM, pp.528-531 (2007).10.1109/ICSSSM.2007.4280175Search in Google Scholar

[18] Luchi D., Rodrigues A.L., Varejao F.M.: Sampling approaches for applying DBSCAN to large datasets. Pattern Recognition Letters, Vol.117, pp.90-96 (2019).10.1016/j.patrec.2018.12.010Search in Google Scholar

[19] Murtagh F.: A survey of recent advances in hierarchical clustering algorithms. Computer Journal, Vol.26, Issue 4, pp.354-359 (1983).10.1093/comjnl/26.4.354Search in Google Scholar

[20] Patrikainen A., Meila M.: Comparing Subspace Clusterings. IEEE Transactions on Knowledge and Data Engineering, Vol.18, Issue 7, pp.902-916 (2006).10.1109/TKDE.2006.106Search in Google Scholar

[21] Pei Z., Xia Hua X., Han J.. The clustering algorithm based on particle swarm optimization algorithm. In Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation, Washington, USA. Vol.1, pp.148-151, (2008).10.1109/ICICTA.2008.421Search in Google Scholar

[22] Rohlf F.: Single-link clustering algorithms. In: P.R Krishnaiah and L.N. Kanal (Eds.), Handbook of Statistics, Vol.2, pp.267-284 (1982).10.1016/S0169-7161(82)02015-XSearch in Google Scholar

[23] Sameh A.S., Asoke K.N.: Development of assessment criteria for clustering algorithms. Pattern Analysis and Applications, Vol.12, Issue 1, pp.79-98 (2009).10.1007/s10044-007-0099-1Search in Google Scholar

[24] Serdah AM., Ashour WM.: Clustering Large-scale Data Based on Modified Affinity Propagation Algorithm. Journal of Artificial Intelligence and Soft Computing Research, Volume 6, Issue 1, pp.23-33, DOI:10.1515/jaiscr-2016-0003 (2016)10.1515/jaiscr-2016-0003Search in Google Scholar

[25] Shah G.H.: An improved dbscan, a density based clustering algorithm with parameter selection for high dimensional data sets. In Nirma University International Engineering,(NUiCONE), pp.1-6 (2012).10.1109/NUICONE.2012.6493211Search in Google Scholar

[26] Sheikholeslam G., Chatterjee S., Zhang A.: WaveCluster: a wavelet-based clustering approach for spatial data in very large databases. The International Journal on Very Large Data Bases, Vol.8 Issue 3-4, pp.289-304 (2000).10.1007/s007780050009Search in Google Scholar

[27] Shieh H-L.: Robust validity index for a modified subtractive clustering algorithm. Applied Soft Computing, Vol.22, pp.47-59 (2014).10.1016/j.asoc.2014.05.001Search in Google Scholar

[28] Smiti A., Elouedi Z.: Dbscan-gm: An improved clustering method based on gaussian means and db-scan techniques. In 16th International Conference on Intelligent Engineering Systems (INES), pp. 573-578, (2012).10.1109/INES.2012.6249802Search in Google Scholar

[29] Soni N., Ganatra A.: AGED (Automatic Generation of Eps for DBSCAN. Int. J. of Computer Science and Information Security, Vol.14, No.5, pp.536-559, (2016).Search in Google Scholar

[30] Starczewski A.: A new validity index for crisp clusters. Pattern Analysis and Applications, Vol.20, Issue 3, pp.687-700 (2017).10.1007/s10044-015-0525-8Search in Google Scholar

[31] Starczewski A., Krzy˙zak A.: A Modification of the Silhouette Index for the Improvement of Cluster Validity Assessment. Lecture Notes in Computer Science, Vol.9693, pp.114-124 (2016).10.1007/978-3-319-39384-1_10Search in Google Scholar

[32] Tsekouras G.E: A simple and effective algorithm for implementing particle swarm optimization in rbf networks design using input-output fuzzy clustering. Neurocomputing, Vol.108, pp.36-44, (2013).10.1016/j.neucom.2012.11.011Search in Google Scholar

[33] Viswanath P., Suresh Babu V.S.: Rough-dbscan: A fast hybrid density based clustering method for large data sets. Pattern Recognition Letters, Vol.30 Issue 16, pp.1477-1488 (2009).10.1016/j.patrec.2009.08.008Search in Google Scholar

[34] Wang W., Yang J., Muntz R.: STING: A Statistical Information Grid Approach to Spatial Data Mining. VLDB ’97 Proceedings of the 23rd International Conference on Very Large Data Bases, pp.186-195 (1997).Search in Google Scholar

[35] Xue-yong L., Guo-hong G., Jia-xia S.: A new intrusion detection method based on improved dbscan. In International Conference on Information Engineering (ICIE), Vol.2, pp.117-120 (2010).10.1109/ICIE.2010.123Search in Google Scholar

[36] Zalik K.R.: An efficient k-means clustering algorithm. Pattern Recognition Letters, Vol.29, Issue 9, pp.1385-1391 (2008).10.1016/j.patrec.2008.02.014Search in Google Scholar

[37] Zhou H., Wang P., Li H.: Research on adaptive parameters determination in DBSCAN algorithm. J. of Information and Computational Science, Vol.9, No.7, pp.1967-1973 (2012).Search in Google Scholar

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