A parameter based growing ensemble of self-organizing maps for outlier detection in healthcare

Christy, A., MeeraGandhi, G., Vaithyasubramanian, S.: Cluster based outlier detection algorithm for healthcare data. Procedia Comput. Sci. 50, 209–215 (2015)

Muhammad, G.: Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Clust. Comput. 18(2), 795–802 (2015)

Vembandasamy, K., Karthikeyan, T.: Novel outlier detection in diabetics classification using data mining techniques. Int. J. Appl. Eng. Res. 11(2), 1400–1403 (2016)

Hu, L., et al.: Software defined healthcare networks. IEEE Wirel. Commun. 22(6), 67–75 (2015)

Hossain, M.S., Muhammad, G., Alamri, A.: Smart healthcare monitoring: a voice pathology detection paradigm for smart cities. Multimed. Syst. (2017). https://doi.org/10.1007/s00530-017-0561-x

Hossain, M.S., Muhammad, G.: Cloud-assisted industrial internet of things (IIoT)—enabled framework for health monitoring. Comput. Netw. 101(2016), 192–202 (2016)

Hossain, M.S., Muhammad, G.: Cloud-assisted speech and face recognition framework for health monitoring. Mob. Netw. Appl. 20(3), 391–399 (2015)

Hu, Y., Duan, K., Zhang, Y. et al.: Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics. Multimed Tools Appl. (2016). https://doi.org/10.1007/s11042-016-3719-1

Hauskrecht, M., Batal, I., Hong, C., Nguyen, Q., Cooper, G.E., Visweswaran, S., Clermont, G.: Outlier-based detection of unusual patient-management actions. An ICU study. J. Biomed. Inform. 64, 211–221 (2017)

Laurikkala, J., Juhola, M., Kentala, E.: Informal identification of outliers in medical data. In: Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2000), A Workshop at the 14th European Conference on Artificial Intelligence (ECAI-2000), Berlin, Germany, August 20–25 (2000)

Hauskrecht, M., Batal, I., Valko, M., Visweswaran, S., Cooper, G.F., Clermont, G.: Outlier detection for patient monitoring and alerting. J. Biomed. Inf. 46(1), 47–55 (2013). https://doi.org/10.1016/j.jbi.2012.08.004

Ypma, R., Duin, P.W.: Novelty detection using self-organizing maps. In: Kasabov, N., Kozma, R., Ko, K., O’Shea, R., Coghill, G., Gedeon, T. (eds.) Progress in Connectionist-Based Information Systems, vol. 2, pp. 1322–1325. Springer, London (1997)

Banerjee, A., Chandola, V., Lazarevic, A., Kumar, V., Srivastava, J.: Anomaly Detection: A Tutorial. In: SIAM Data Mining Conference, Atlanta, GA (2008)

Song, X., Wu, M., Jermaine, C., Ranka, S.: Conditional anomaly detection. IEEE Trans. Knowl. Data Eng. 19(5), 631–645 (2007)

Olivetti & Oracle Research Laboratory, The Olivetti & Oracle Research Laboratory Face Database of Faces. http://www.cam-orl.co.uk/facedatabase.html

TILDA, Textile defect image database. University of Freiburg, Germany (1996)

Geman, S., et al.: Neural networks and the bias/variance dilemma. Neural Comput. 4, 1–58 (1992)

Zhang, Y., Meratnia, N., Havinga, P.J.M.: Outlier Detection Techniques for Wireless Sensor Network: A Survey. University of Twente, Enschede (2008)

Ghaemi, R., Sulaiman, M.N., Ibrahim, I., Mustapha, N.: A Survey: Clustering Ensembles Techniques. World Academy of Science, Engineering and Technology, Singapore (2009)

Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: KDD, pp. 157–166 (2005)

Hellerstein, J.M.: Quantitative data cleaning for large databases. http://db.cs.berkeley.edu/jmh/papers/cleaning-unece.pdf (Last visited in 2010)

Hodge, V.J., Austin, J.A.: Survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)

Fausette, V.L.: Fundamentals of Neural Networks. Prentice Hall, Upper Saddle River (1993)

Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: Jagadish, H.V., Mumick, I.S. (Eds.). Proceedings of the ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4-6, pp. 103–114. ACM Press, New York (1996)

Ester, M., Kriegel, H-P., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, pp. 226–231 (1996)

Stolfo, S.J., Prodromidis, A.L., Tselepis, S., Lee, W., Fan, D.W., Chan, P.K.: JAM: Java agents for meta-learning over distributed databases. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 74–81 (1997)

Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth International Group, Belmont, CA (1984)

Cohen, W.W.: Fast effective rule induction. In: International Conference on Machine Learning, pp. 115–123 (1995)

Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-based outliers: algorithms and applications. VLDB J. 8, 237–253 (2000)

Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. J. Artif. Intell. Res. 11, 131–167 (1999)

Saunders, R., Gero, J.S.: A curious design agent: a computational model of novelty-seeking behavior in design. In: Proceedings of the Sixth Conference on Computer Aided Architectural Design Research in Asia (CAADRIA2001), Sydney, pp. 725–738(2001a)

Vesanto, J., Himberg, J., Siponen, M., Simula, O.: Enhancing SOM based data visualization. In: Proceedings of the 5th International Conference on Soft Computing and Information/Intelligent Systems. Methodologies for the Conception, Design and Application of Soft Computing, vol. 1, pp. 64–67. Singapore: World Scientific (1998)

Graham, W., Rohan, B., Hongxing, H., Hawkins, S., Gu, L.: A comparative study of RNN for outlier detection in data mining. In: ICDM ’02 Proceedings of the 2002 IEEE International Conference on Data Mining IEEE Computer Society Washington, DC, USA (2002)

Hawkins, S., Hongxing, H., Graham, W., Rohan, B., Baxter, A.: Outlier Detection Using Replicator Neural Networks, DaWaK, pp. 170–180. Springer, New York (2002)

Kohonen, T.: Self-Organizing Maps. Springer, New York (2001)

Jiawei, H., Micheline, K., Pei, P.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier, New York (2010)

Saunders, R., Gero, J.S.: Designing for interest and novelty: motivating design agents. In: Proceedings of CAAD Futures 2001, pp. 725–738. Eindhoven (2001)

Marsland, S.: On-line novelty detection through self-organization, with application to inspection robotics. Ph.D. thesis, Faculty of Science and Engineering, University of Manchester, UK (2001)

Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorization. J. Inf. Fusion 6(1), 5–20 (2005)

Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Mach. Learn. 51, 181–207 (2003)

Savdra, C., Salas, R., Moreno, S., Allende, H.: Fusion of self organizing maps. In: Prudhomme et al. (eds.) LNCS 4507, (2007); ISMIS, LNAI 4994 (2008)

Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-Organizing Map in Matlab: the SOM Toolbox. In: Proceedings of the Matlab DSP Conference, pp. 35–40. Espoo, Finland (1999)

Moglu, F., Alpaydin, E.: Combining multiple representations for pen-based handwritten digit recognition. Turk J. Electr. Eng. 9(1) (2001)

Xue, Z., Shang, Y., Feng, A.: Semi-supervised outlier detection based on fuzzy rough C-means clustering. Math Comput. Simul. 80(9) (2010)

Buizza, R., Palmer, T.N.: Impact of Ensemble Size on Ensemble Prediction, European Centre for Medium-Range Weather Forecasts, Reading, Berkshire, UK (1988)

UC Irvine machine learning repository. http://archive.ics.uci.edu/ml/index.html (2010)