Subpopulation Discovery in Epidemiological Data with Subspace Clustering

[1] B. Preim, P. Klemm, H. Hauser, K. Hegenscheid, S. Oeltze, K. Toennies, and H. Völzke, Visualization in Medicine and Life Sciences III, ch. Visual Analytics of Image-Centric Cohort Studies in Epidemiology. Springer, 2014.Search in Google Scholar

[2] A. D. Hingorani, D. A. van der Windt, R. D. Riley, (...), W. Sauerbrei, D. G. Altman, and H. Hemingway, “Prognosis research strategy (PROGRESS) 4: Stratified medicine research,” BMJ: British Medical Journal, vol. 346, no. e5793, 2013.10.1136/bmj.e5793356568623386361Search in Google Scholar

[3] H. Völzke, C. Schmidt, K. Hegenscheid, J. Kühn, F. Bamberg, W. Lieb, H. Kroemer, N. Hosten, and R. Puls, “Population imaging as valuable tool for personalized medicine,” Clin Pharmacol Ther, vol. 92, no. 4, pp. 422-424, 2012.10.1038/clpt.2012.10022910443Search in Google Scholar

[4] H. Völzke, D. Alte, . . . , R. Biffar, U. John, and W. Hoffmann, “Cohort profile: the Study of Health In Pomerania,” International Journal of Epidemiology, vol. 40, no. 2, pp. 294-307, 2011.Search in Google Scholar

[5] L. Parsons, E. Haque, and H. Liu, “Subspace Clustering for High Dimensional Data: A Review,” ACM SIGKDD Explorations Newsletter, vol. 6, pp. 90-105, 2004.10.1145/1007730.1007731Search in Google Scholar

[6] K. Sim, V. Gopalkrishnan, A. Zimek, and G. Cong, “A survey on enhanced subspace clustering,” Data mining and knowledge discovery, vol. 26, pp. 332-397, 2013.10.1007/s10618-012-0258-xSearch in Google Scholar

[7] A. Zimek, Data Clustering: Algorithms and Applications, ch. Clustering High- Dimensional Data, pp. 201-230. CRC Press, 2013.10.1201/9781315373515-9Search in Google Scholar

[8] C. Zhang and R. L. Kodell, “Subpopulation-specific confidence designation for more informative biomedical classification,” Artificial Intelligence in Medicine, vol. 58, no. 3, pp. 155-163, 2013.10.1016/j.artmed.2013.04.008372724423731649Search in Google Scholar

[9] S. Glaßer, U. Niemann, B. Preim, and M. Spiliopoulou, “Can we Distinguish Between Benign and Malignant Breast Tumors in DCE-MRI by Studying a Tumor's Most Suspect Region Only?,” in 26th International Symposium on Computer- Based Medical Systems (CBMS), pp. 77-82, 2013.10.1109/CBMS.2013.6627768Search in Google Scholar

[10] U. Niemann, H. Völzke, J.-P. Kühn, and M. Spiliopoulou, “Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis,” Expert Systems with Applications, vol. 41, pp. 5405-5415, September 2014.Search in Google Scholar

[11] T. Hielscher, M. Spiliopoulou, H. Völzke, and J.-P. Kühn, “Using participant similarity for the classification of epidemiological data on hepatic steatosis,” in Proc. of the 27th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS'14), pp. 1-7, IEEE, 2014.10.1109/CBMS.2014.28Search in Google Scholar

[12] M. A. Hall, “Correlation-based feature selection for discrete and numeric class machine learning,” in Proc. of 17th Int. Conf. on Machine Learning, pp. 359-366, Morgan Kaufmann, 2000.Search in Google Scholar

[13] P. Klemm, L. Frauenstein, D. Perlich, K. Hegenscheid, H. Völzke, and B. Preim, “Clustering Socio-demographic and Medical Attribute Data in Cohort Studies,” in Bildverarbeitung für die Medizin (BVM), pp. 180-185, Springer Berlin Heidelberg, 2014.10.1007/978-3-642-54111-7_36Search in Google Scholar

[14] R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, “Automatic subspace clustering of high dimensional data for data mining applications,” in Proceedings of the ACM International Conference on Management of Data (SIGMOD), pp. 61-72, 1998.10.1145/276304.276314Search in Google Scholar

[15] C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park, “Fast Algorithms for Projected Clustering,” in Proceedings of the ACM International Conference on Management of Data (SIGMOD), pp. 61-72, 1999.10.1145/304181.304188Search in Google Scholar

[16] D. Damian, M. Orešič, E. Verheij, J. Meulman, J. Friedman, A. Adourian, N. Morel, A. Smilde, and J. van der Greef, “Applications of a new subspace clustering algorithm (COSA) in medical systems biology,” Metabolomics, vol. 3, no. 1, pp. 69-77, 2007.10.1007/s11306-006-0045-zSearch in Google Scholar

[17] L. S. Friedman and E. B. Keeffe, Handbook of Liver Disease. Library of Congress Cataloging-in-Publication Data, 2011.Search in Google Scholar

[18] A. P. Levene and R. D. Goldin, “The epidemiology, pathogenesis and histopathology of fatty liver disease,” Histopathology, vol. 61, pp. 141-152, 2012.10.1111/j.1365-2559.2011.04145.x22372457Search in Google Scholar

[19] S. Bellentani, G. Bedogni, L.Miglioli, and C. Tiribelli, “The epidemiology of fatty liver,” European Journal of Gastroenterology & Hepatology, vol. 16, pp. 1087-1093, 2004.Search in Google Scholar

[20] G. Bedogni, S. Bellentani, L. Miglioli, F. Masutti, M. Passalacqua, A. Castiglione, and C. Tiribelli, “The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population,” BMC Gastroenterology, vol. 6, no. 33, 2006.10.1186/1471-230X-6-33163665117081293Search in Google Scholar

[21] X. Yuan, D. Waterworth, J. R. Perry, (...), T. M. Frayling, J. S. Kooner, and V. Mooser, “Impact of fatty liver disease on health care utilization and costs in a general population: A 5-year observation,” Gastroenterology, vol. 134, no. 1, pp. 85-94, 2008.10.1053/j.gastro.2007.10.02418005961Search in Google Scholar

[22] H. Völzke, S. Schwarz, S. E. Baumeister, H. Wallaschofski, C. Schwahn, H. J. Grabe, T. Kohlmann, U. John, and M. Dören, “Menopausal status and hepatic steatosis in a general female population,” Gut, vol. 56, pp. 594-595, 2007.10.1136/gut.2006.115345185685217369390Search in Google Scholar

[23] S. Baumeister, H. Völzke, P. Marschall, U. John, C. Schmidt, and D. Alte, “Impact of fatty liver disease on health care utilization and costs in the general population: a 5-year observation,” Gastroenterology, vol. 134, pp. 85-94, 2008.10.1053/j.gastro.2007.10.024Search in Google Scholar

[24] J.-P. Kühn, D. Hernando, B. Mensel, (...), J. Mayerle, N. Hosten, and S. B. Reeder, “Quantitative chemical shift-encoded MRI is an accurate method to quantify hepatic steatosis,” Journal of Magnetic Resonance Imaging, vol. 39, no. 6, pp. 1494-1501, 2014.Search in Google Scholar

[25] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques Third Edition. Morgan Kaufmann Publishers, 2012.Search in Google Scholar

[26] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “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 (KDD-96), pp. 226-231, 1996.Search in Google Scholar

[27] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179-188, 1936.10.1111/j.1469-1809.1936.tb02137.xSearch in Google Scholar

[28] V. G. Sigillito, S. P. Wing, L. V. Hutton, and K. B. Baker, “Classification of radar returns from the ionosphere using neural networks,” Johns Hopkins APL Tech. Dig, vol. 10, pp. 262-266, 1989.Search in Google Scholar

[29] D. Dias, R. Madeo, T. Rocha, H. Biscaro, and S. Peres, “Hand movement recognition for brazilian sign language: A study using distance-based neural networks,” in International Joint Conference on Neural Networks (IJCNN 2009), pp. 697-704, 2009.10.1109/IJCNN.2009.5178917Search in Google Scholar

[30] K. Kailing, H.-P. Kriegel, and P. Kröger, “Density-Connected Subspace Clustering for High-Dimensional Data,” in Proc. SIAM Int. Conf. on Data Mining (SDM'04), pp. 246-257, 2004.10.1137/1.9781611972740.23Search in Google Scholar

[31] I. Assent, R. Krieger, E. Müller, and T. Seidl, “DUSC: Dimensionality Unbiased Subspace Clustering,” in ICDM, pp. 409-414, 2007.10.1109/ICDM.2007.49Search in Google Scholar

[32] U. Niemann, “The potential of high-dimensional clustering for subpopulation discovery in epidemiological datasets.” Otto-von-Guericke University Magdeburg, Faculty of Computer Science, 2014. Master Thesis.Search in Google Scholar

[33] D. R. Wilson and T. R. Martinez, “Improved heterogeneous distance functions,” J. Artif. Int. Res., vol. 6, pp. 1-34, Jan. 1997.10.1613/jair.346Search in Google Scholar

[34] P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. Pearson/Addison-Wesley, 2006.Search in Google Scholar

[35] P. J. Hanly and S. B. Ahmed, “Sleep Apnea and the Kidney: is sleep apnea a risk factor for chronic kidney disease?,” CHEST Journal, vol. 146, no. 4, pp. 1114-1122, 2014.Search in Google Scholar

[36] J. Zhao, “Subspace clustering with gravitation.,” in Grundlagen von Datenbanken, 2010.Search in Google Scholar

[37] J. Zhao and S. Conrad, “Automatic subspace clustering with density function.,” in DATA, pp. 63-69, 2012.Search in Google Scholar

[38] E. Müller, I. Assent, S. Günnemann, R. Krieger, and T. Seidl, “Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data,” in Ninth IEEE International Conference on Data Mining (ICDM'09), pp. 377-386, IEEE, 2009.10.1109/ICDM.2009.10Search in Google Scholar

[39] S. Günnemann, E. Müller, I. Färber, and T. Seidl, “Detection of orthogonal concepts in subspaces of high dimensional data,” in Proceedings of the 18th ACM conference on Information and knowledge management, pp. 1317-1326, ACM, 2009.Search in Google Scholar

[40] G. Moise and J. Sander, “Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining: ACM, pp. 533-541, 2008.10.1145/1401890.1401956Search in Google Scholar

[41] U. Fayyad and K. Irani, “Multi-interval discretization of continuous-valued attributes for classification learning,” in Proc. of 17th Int. Conf. on Machine Learning, pp. 1022-1029, Morgan Kaufmann, 1993.Search in Google Scholar

[42] M. J. Zaki, M. Peters, I. Assent, and T. Seidl, “Clicks: An effective algorithm for mining subspace clusters in categorical datasets,” Data & Knowledge Engineering, vol. 60, no. 1, pp. 51-70, 2007.10.1016/j.datak.2006.01.005Search in Google Scholar

[43] G. Gan and J. Wu, “Subspace clustering for high dimensional categorical data,” ACM SIGKDD Explorations Newsletter, vol. 6, no. 2, pp. 87-94, 2004.10.1145/1046456.1046468Search in Google Scholar

[44] E. Müller, I. Assent, and T. Seidl, “HSM: Heterogeneous subspace mining in high dimensional data,” in Scientific and Statistical Database Management, pp. 497-516, Springer, 2009.10.1007/978-3-642-02279-1_36Search in Google Scholar

[45] F. Cao, J. Liang, D. Li, and X. Zhao, “A weighting k-modes algorithm for subspace clustering of categorical data,” Neurocomputing, vol. 108, pp. 23-30, 2013.10.1016/j.neucom.2012.11.009Search in Google Scholar

[46] I. Färber, S. Günnemann, H.-P. Kriegel, P. Kröger, E. Müller, E. Schubert, T. Seidl, and A. Zimek, “On using class-labels in evaluation of clusterings,” in MultiClust: 1st International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD, 2010. Search in Google Scholar