[1. FRIEDMAN, J. H., 2016. Data mining and statistics: What‘s the connection? Stanford: Stanford University, CA 94305. [10.11.2016] available at: http://statweb.stanford.edu/~jhf/ftp/dm-stat.pdf]Search in Google Scholar
[2. BABCOCK, B., DATAR, M., MOTWANI, R., O’CALLAGHAN, L., 2003. Maintaining variance and k-medians over data stream windows. In: Proc. ACM Symp. on Principles of Database Systems.10.1145/773153.773176]Search in Google Scholar
[3. DATTORRO, J., 2008. Equality relating Euclidean distance cone to positive semidefinite cone. Linear Algebra and its Applications, 428, 2597–2600. [10.11.2016] available at: https://ccrma.stanford.edu/~dattorro/EDM.pdf10.1016/j.laa.2007.12.008]Search in Google Scholar
[4. NAZARI, Z., et all., 2015. A New Hierarchical Clustering Algorithm. In: ICIIBMS 2015, Track2: Artificial Intelligence, Robotics, and Human-Computer Interaction. Okinawa, Japan [10.11.2016].10.1109/ICIIBMS.2015.7439517]Search in Google Scholar
[5. ALPYDIN, E., 2010. Introduction to Machine Learning. The MIT Press, pp. 143-158.]Search in Google Scholar
[6. FAYYAD, U. et al., 1996. From Data Mining to Knowledge Discovery in Databases. American Association for Artificial Intelligence, 0738-4602.]Search in Google Scholar
[7. KOVALERCHUK, B., VITYAEV, E., 2000. Data Mining in Finance: Advances in Relational and Hybrid Methods. Springer Science & Business Media, 2000 edition, ISBN-10: 0792378040, ISBN-13: 978-0792378044]Search in Google Scholar