Abstract
Outlier detection is an important task in data mining with numerous applications. Recent years, the study on outlier detection is very active, many algorithms were proposed including that based on clustering. However, most outlier detection algorithms based on clustering often need parameters, and it is very difficult to select a suitable parameter for different data set. In order to solve this problem, an outlier detection algorithm called outlier detection based on cluster outlier factor and mutual density is proposed in this paper which combining the natural neighbor search algorithm of the Natural Outlier Factor (NOF) algorithm and based on the Density and Distance Cluster (DDC) algorithm. The mutual density and γ density is used to construct decision graph. The data points with γ density anomalously large in decision graph are treated as cluster centers. This algorithm detect the boundary of outlier cluster using cluster outlier factor called Cluster Outlier Factor (COF), it can automatic find the parameter. This method can achieve good performance in clustering and outlier detection which be shown in the experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2001). 5(4):394–395 (2006, in Chinese)
Denning, D.E.: An intrusion-detection model. IEEE Trans. Softw. Eng. SE-13(2), 222–232 (2006)
Bolton, R.J., David, J.H.: Unsupervised profiling methods for fraud detection. In: Proceedings of Credit Scoring & Credit Control VII, pp. 5–7 (2001)
Laurikkala, J., Juhola, M., Kentala, E.: Informal identification of outliers in medical data. In: Intelligent Data Analysis in Medicine & Pharmacology (2000)
Lin, J., Keogh, E., Fu, A., et al.: Approximations to magic: finding unusual medical time series. In: 2005 Proceedings of IEEE Symposium on Computer-Based Medical Systems, pp. 329–334. IEEE (2005)
Zhao, J., Lu, C.T., Kou, Y.: Detecting region outliers in meteorological data, pp. 49–55 (2003)
Bhattacharya, G., Ghosh, K., Chowdhury, A.S.: Outlier detection using neighborhood rank difference, pp. 24–31. Elsevier Science Inc. (2015)
Xue, A.-R., Ju, S.-G., He, W.-H., et al.: Study on algorithms for local outlier detection. Chinese J. Comput. 30(8), 1455–1463 (2007)
Wang, Y., Zhang, J.-F., Zhao, X.-J.: Contextual outlier mining algorithm based on particle swarm optimization. J. Taiyuan Univ. Sci. Technol. 36(5), 327–332 (2015)
Hawkins, D.M.: Identification of outliers. Biometrics 37(4), 860 (1980)
Xu, X., Liu, J.-W., Luo, X.-L.: Research on outlier mining. Appl. Res. Comput. 26(1), 34–40 (2009). (in Chinese)
Breunig, M.M., Kriegel, H.P., Ng, R.T., et al.: LOF: identifying density-based local outliers. ACM SIGMOD Rec. 29(2), 93–104 (2000)
Ha, J., Seok, S., Lee, J.S.: Robust outlier detection using the instability factor. Knowl.-Based Syst. 63(2), 15–23 (2014)
Jin, W., Tung, A.K.H., Han, J., Wang, W.: Ranking outliers using symmetric neighborhood relationship. In: Ng, W.K., Kitsuregawa, M., Li, J., Chang, K. (eds.) Advances in Knowledge Discovery and Data Mining. LNCS, vol. 3918, pp. 577–593. Springer, Heidelberg (2006). https://doi.org/10.1007/11731139_68
Tao, J.: Clustering-based and density outlier detection method. Master dissertation of South China University of Technology, pp. 1–56 (2014, in Chinese)
Huang, J., Zhu, Q., Yang, L., et al.: A non-parameter outlier detection algorithm based on Natural Neighbor. Knowl.-Based Syst. 92(C), 71–77 (2016)
Rodriguez, A., Laio, A.: Machine learning. Clustering by fast search and find of density peaks. Science 344(6191), 1492 (2014)
Huang, J., Zhu, Q., Yang, L., et al.: A novel outlier cluster detection algorithm without top-n parameter. Knowl.-Based Syst. 121, 32–40 (2017)
Veenman, C.J., Reinders, M.J.T., Backer, E.: A maximum variance cluster algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1273–1280 (2002)
Fu, L., Medico, E.: FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinform. 8(1), 3 (2007)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Z., Zhu, M., Qiu, J., Liu, C., Zhang, D., Qi, J. (2019). Outlier Detection Based on Cluster Outlier Factor and Mutual Density. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, Singapore. https://doi.org/10.1007/978-981-13-6473-0_28
Download citation
DOI: https://doi.org/10.1007/978-981-13-6473-0_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6472-3
Online ISBN: 978-981-13-6473-0
eBook Packages: Computer ScienceComputer Science (R0)