Outlier mining using the DBSCAN algorithm

• Autorzy: Nowak-Brzezińska A. , Xięski T.
• Języki publikacji: EN

Abstrakty

EN

This paper introduces an approach to outlier mining in the context of a real-world dataset containing information about the mobile transceivers operation. The goal of the paper is to analyze the influence of using different similarity measures and multiple values of input parameters for the densitybased clustering algorithm on the number of outliers discovered during the mining process. The results of the experiments are presented in section 4 in order to discuss the significance of the analyzed parameters.

Słowa kluczowe

EN

outlier detection; similarity analysis; clustering; knowledge-based systems

PL

wykrywanie wyjątków; analiza podobieństwa; grupowanie; system oparty na wiedzy

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2017
• Tom: Vol. 25, nr 2
• Strony: 53--68
• Opis fizyczny: Bibliogr. 12 poz., 1 wykr.

Twórcy

autor

Nowak-Brzezińska A.

• Institute of Computer Science, University of Silesia in Katowice, Bankowa 12, 40-007 Katowice, Poland

autor

Xięski T.

• Institute of Computer Science, University of Silesia in Katowice, Bankowa 12, 40-007 Katowice, Poland

Bibliografia

• [1] Aggarwal, C. C., Outlier Analysis, Springer, 2013.
• [2] Aggarwal, C. C., Data Mining - The Textbook, Springer, 2015.
• [3] Aggarwal, C. C. and Sathe, S., Outlier Ensembles - An Introduction, Springer, 2017.
• [4] Tan, P.-N., Steinbach, M., and Kumar, V., Introduction to Data Mining, Addison-Wesley Longman Publishing Co., Inc., 2005.
• [5] Zhang, J., Advancements of Outlier Detection: A Survey, EAI Endorsed Transactions on Scalable Information Systems, Vol. 13, No. 1, 2 2013.
• [6] Ester, M., Kriegel, H.-P., Sander, J., and Xu, X., A density-based algorithm for discovering clusters in large spatial databases with noise, In: In Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, AAAI Press, 1996, pp. 226–231.
• [7] Nowak-Brzezińska, A. and Xięski, T., Exploratory Clustering and Visualization, Procedia Computer Science, Vol. Volume/issue: 35C, 2014, pp. 1082–1091.
• [8] Xia, T. and Zhang, D., Improving the R*-tree with Outlier Handling Techniques, In: Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems, GIS ’05, ACM, New York, NY, USA, 2005, pp. 125–134.
• [9] Wakulicz-Deja, A., Nowak-Brzezińska, A., and Xięski, T., Effciency of Complex Data Clustering, In: Proceedings of the 6th International Conference on Rough Sets and Knowledge Technology, Springer-Verlag, 2011, pp. 636–641.
• [10] Wakulicz-Deja, A., Nowak-Brzezińska, A., and Xięski, T., Density-Based Method for Clustering and Visualization of Complex Data, In: Proceedings of the 8th International Conference RSCTC 2012, Springer-Verlag, 2012, pp. 142–149.
• [11] Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Inc., 2011.
• [12] Guha, S., Rastogi, R., and Shim, K., CURE: An Efficient Clustering Algorithm for Large Databases, In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, ACM, 1998, pp. 73–84.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).