[
[1] Agrawal R., Gehrke J., Gunopulos D., Raghavan P.: Automatic subspace clustering of high dimensional data for data mining applications. SIGMOD Rec., vol. 27, pp. 94-105 (1998).
]Search in Google Scholar
[
[2] Boonchoo T., Ao X., Liu Y., Zhao W., He Q.: Grid-based DBSCAN: Indexing and inference. Pattern Recognition, Vol. 90, pp.271-284 (2019).
]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 Y., Tang S., Bouguila N., Wanga C., Du J., Li H.: A fast clustering algorithm based on pruning unnecessary distance computations in DB-SCAN for high-dimensional data. Pattern Recognition, Vol.83, pp.375-387 (2018).
]Search in Google Scholar
[
[5] Darong H., Peng W.: Grid-based dbscan algorithm with referential parameters. Physics Procedia, 24, Part B, pp.1166-1170 (2012).
]Search in Google Scholar
[
[6] 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
[
[7] Fränti P., Rezaei M., Zhao Q.: Centroid index: Cluster level similarity measure. Pattern Recognition, Vol. 47, Issue 9, pp. 3034-3045 (2014).
]Search in Google Scholar
[
[8] Gabryel M.: Data Analysis Algorithm for Click Fraud Recognition. Communications in Computer and Information Science, Vol 920, pp.437-446 (2018).
]Search in Google Scholar
[
[9] Gan J., Tao Y.: Dbscan revisited: mis-claim, unfixability, and approximation. SIGMOD (2015).
]Search in Google Scholar
[
[10] Grycuk R., Najgebauer P., Kordos M., Scherer M., Marchlewska A.: Fast Image Index for Database Management Engines. Journal of Artificial Intelligence and Soft Computing Research, Vol. 10, Issue 2, pp.113 - 123 (2020)10.2478/jaiscr-2020-0008
]Search in Google Scholar
[
[11] 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. pp. 403-406, IEEE (2004).
]Search in Google Scholar
[
[12] Karami A., Johansson R.: Choosing DBSCAN Parameters Automatically using Differential Evolution. International Journal of Computer Applications, Vol. 91, pp.1-11 (2014)10.5120/15890-5059
]Search in Google Scholar
[
[13] Kumar K.M., Reddy A.R.M.: A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method. Pattern Recognition, vol 58, pp.39-48 (2016).
]Search in Google Scholar
[
[14] Liu F., Wen P. and Zhu E.: Efficient Grid-based Clustering Algorithm with Leaping Search and Merge Neighbors Method. IOP Conf. Series: Materials Science and Engineering, vol. 242 (2017)10.1088/1757-899X/242/1/012122
]Search in Google Scholar
[
[15] 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).
]Search in Google Scholar
[
[16] Meng X., van Dyk D.: The EM algorithm - An old folk-song sung to a fast new tune. Journal of the Royal Statistical Society, Series B (Methodological) Vol. 59, Issue 3, pp. 511-567 (1997).
]Search in Google Scholar
[
[17] Murtagh F.: A survey of recent advances in hierarchical clustering algorithms. Computer Journal, Vol. 26, Issue 4, pp. 354-359 (1983).
]Search in Google Scholar
[
[18] Patrikainen A., Meila M.: Comparing Subspace Clusterings, IEEE Transactions on Knowledge and Data Engineering, Vol.18, Issue 7, pp.902-916 (2006).
]Search in Google Scholar
[
[19] 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-X
]Search in Google Scholar
[
[20] 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).
]Search in Google Scholar
[
[21] 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).
]Search in Google Scholar
[
[22] 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).
]Search in Google Scholar
[
[23] Shieh H-L.: Robust validity index for a modified subtractive clustering algorithm. Applied Soft Computing, Vol. 22, pp. 47-59 (2014).
]Search in Google Scholar
[
[24] Starczewski A.: A new validity index for crisp clusters. Pattern Analysis and Applications, Vol.20, Issue 3, pp. 687-700 (2017).
]Search in Google Scholar
[
[25] Starczewski A., Cader A.: Determining the Eps Parameter of the DBSCAN Algorithm Lecture Notes in Computer Science, Vol. 11509, pp. 420-430 (2019).
]Search in Google Scholar
[
[26] 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
[
[27] 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).
]Search in Google Scholar
[
[28] Zalik K.R.: An efficient k-means clustering algorithm. Pattern Recognition Letters, Vol.29, Issue 9, pp.1385-1391 (2008).
]Search in Google Scholar
