Słowa kluczowe
similarity analysis
clustering
knowledge-based systems
Jak cytować
Abstrakt
This paper introduces an approach to outlier mining in the context of rule-based knowledge bases. Rules in knowledge bases are a very specific type of data representation and it is necessary to analyze them carefully, especially when they differ from each other. The goal of the paper is to analyze the influence of using different similarity measures and clustering methods on the number of outliers discovered during the mining process. The results of the experiments are presented in Section 6 in order to discuss the significance of the analyzed parameters.
Bibliografia
Portnoy, L., Eskin, E., and Stolfo, S., Intrusion detection with unlabeled data using clustering, In: In Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001, 2001, pp. 5–8.
Pedrycz, W., Knowledge-based clustering - from data to information granules, Wiley, 2005.
Grzymala-Busse, J. W., A New Version of the Rule Induction System LERS, Fundam. Inf., Vol. 31, No. 1, July 1997, pp. 27–39.
Bazan, J. G., Szczuka, M. S., and Wroblewski, J., A New Version of Rough Set Exploration System, In: Rough Sets and Current Trends in Computing, Third International Conference, RSCTC 2002, Malvern, PA, USA, October 14-16, 2002, Proceedings, 2002, pp. 397–404.
Nowak-Brzezinska, A., ´ Mining Rule-based Knowledge Bases Inspired by Rough Set Theory, Fundamenta Informaticae, Vol. 148, No. 35, 2016, pp. 35–50.
Duraj, A., Szczepaniak, P., and Ochelska-Mierzejewska, J., Detection of Outlier Information Using Linguistic Summarization, In: Flexible Query Answering Systems 2015; Advances in Intelligent Systems and Computing 400,(Eds.: Andreasen T., et al.), Proceedings of the 11th International Conference FQAS 2015, 2016, pp. 101–113.
Hawkins, D., Identification of Outliers, Chapman and Hall, 1980.
Aggarwal, C. C. and Sathe, S., Outlier Ensembles - An Introduction, Springer, 2017.
Aggarwal, C. C., Data Mining - The Textbook, Springer, 2015.
Aggarwal, C. C., Outlier Analysis, Springer, 2013.
Tukey, J. W., Exploratory Data Analysis, Addison-Wesley, 1977.
Jain, A. K., Murty, M., and Flynn, P., Data clustering: A review, ACM Computing Surveys, Vol. 31, No. 3, 1999, pp. 264–323.
Münz, G., Li, S., and Carle, G., Traffic Anomaly Detection Using KMeans Clustering, In: In GI/ITG Workshop MMBnet, 2007.
Leung, K. and Leckie, C., Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters, In: Proceedings of the Twenty-eighth Australasian Conference on Computer Science - Volume 38, ACSC ’05, Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 2005, pp. 333–342.
Syarif, I., Prugel-Bennett, A., and Wills, G., Unsupervised Clustering Approach for Network Anomaly Detection, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 135–145.
Jain, A. K. and Law, M. H. C., Data Clustering: A User’s Dilemma, Springer Berlin Heidelberg, Berlin, Heidelberg, 2005, pp. 1–10.
Boriah, S., Chandola, V., and Kumar, V., Similarity measures for categorical data: A comparative evaluation, In: In Proceedings of the eighth SIAM International Conference on Data Mining, pp. 243–254.
Nowak-Brzezinska, A. and Rybotycki, T., ´ Visualization of medical rulebased knowledge bases, Journal of Medical Informatics & Technologies, Vol. 24, 2015, pp. 91–98.
Gower, J. C. and Gower, J. C., A general coefficient of similarity and some of its properties, Biometrics, 1971.
Asuncion, A. and Newman, D., UCI Machine Learning Repository, 2007.