Visual analytics for concept exploration in subspaces of patient groups
Tóm tắt
Từ khóa
Tài liệu tham khảo
Holzinger A, Dehmer M, Jurisica I (2014) Knowledge discovery and interactive data mining in bioinformatics—state-of-the-art, future challenges and research directions. BMC Bioinform 15(S6):I1
Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When is “nearest neighbor” meaningful? In: Proceedings of International Conference on Database Theory, pp 217–235
Hinneburg A, Aggarwal CC, Keim DA (2000) What is the nearest neighbor in high dimensional spaces? In: Proceedings of international conference on very large data bases, pp 506–515
Parsons L, Haque E, Liu H (2004) Subspace clustering for high dimensional data: a review. SIGKDD Explor 6(1):90–105
Hund M, Behrisch M, Färber I, Sedlmair S, Schreck T, Seidl T, Keim DA (2015) Subspace nearest neighbor search - problem statement, approaches, and discussion. In: Similarity search and applications (LNCS 9371), pp 307–313
Ward MO, Grinstein G, Keim GA (2010) Interactive data visualization: foundations, techniques, and applications. CRC Press, Boca Ratan
Cook KA, Thomas JJ (2005) Illuminating the path: the research and development agenda for visual analytics. IEEE Computer Society
Keim DA, Mansmann F, SchneidewindJ, Thomas J, Ziegler H (2008) Visual analytics: scope and challenges. In: Visual data mining: theory, techniques and tools for visual analytics (LNCS 4404), pp 76–90
Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco
Jolliffe I (2002) Principal component analysis. Wiley Online Library
Kriegel H-P, Kröger P, Zimek A (2009) Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans Knowl Discov Data (TKDD) 3(1):1–58
Sedlmair M, Heinzl C, Bruckner S, Piringer H, Moller Torsten (2014) Visual parameter space analysis: a conceptual framework. IEEE Trans Vis Comput Graphics (TVCG) 20(12):2161–2170
Fua YH, Ward MO, Rundensteiner EA (1999) Hierarchical parallel coordinates for exploration of large data sets. In: Proceedings of Conference on Visualization, pp 43–50
Buja A, Littman ML, Dean N, Hofmann H, Chen L (2008) Data visualization with multidimensional scaling. J Comput Graphical Stat 17(2):444–472
Seo J, Shneiderman B (2002) Interactively exploring hierarchical clustering results. Computer 35(7):80–86
Bremm S, Von Landesberger T, Heß M, Schreck T, Weil P, Hamacher K (2011) Interactive visual comparison of multiple trees. In: Proceedings of IEEE symposium on visual analytics science and technology (VAST), pp 31–40
Bremm S, von Landesberger T, Bernard J, Schreck T (2011) Assisted descriptor selection based on visual comparative data analysis. Comput Graphics Forum 30(3):891–900
Assent I, Krieger R, Müller E, Seidl T (2007) Visa: visual subspace clustering analysis. ACM SIGKDD Explor Newslett 9(2):5–12
Müller E, Assent I, Krieger R, Jansen T, Seidl T (2008) Morpheus: interactive exploration of subspace clustering. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1089–1092
Günnemann S, Färber I, Kremer H, Seidl T (2010) Coda: interactive cluster based concept discovery. Proc VLDB Endow 3(1–2):1633–1636
Gunnemann S, Kremer H, Färber I, Seidl T (2010) MCExplorer: interactive exploration of multiple (Subspace) clustering solutions. In: Data Mining Workshops (ICDMW), 2010 IEEE international conference on, pp 1387–1390
Tatu A, Zhang L, Bertini E, Schreck T, Keim Daniel, Bremm Sebastian, von Landesberger Tatiana (2012) Clustnails: visual analysis of subspace clusters. Tsinghua Sci Technol 17(4):419–428
Andrada T, Fabian M, Ines F, Enrico B, Tobias S, Thomas S, Keim Daniel A (2012) Subspace search and visualization to make sense of alternative clusterings in high-dimensional data. In: Proceedings of IEEE conference visual analytics, science and technology, pp 63–72
Turkay Cagatay, Lex Alexander, Streit Marc, Pfister Hanspeter, Hauser Helwig (2014) Characterizing cancer subtypes using dual analysis in caleydo StratomeX. IEEE Comput Graphics Appl 34(2):38–47
Rind A, Aigner W, Miksch S, Wongsuphasawat K, Plaisant C, Shneiderman B (2011) Interactive information visualization to explore and query electronic health records. Found Trends Human–Computer Interact 5(3):207–298
Mittelstädt S, Hao MC, Dayal U, Hsu M, Terdiman J, Keim DA (2014) Advanced visual analytics interfaces for adverse drug event detection. In: Proceedings of the working conference on advanced visual interfaces (AVI), pp 237–244
Suominen H, Schreck T, Leroy G, Hochheiser H, Goeuriot L, Kelly L, Mowery D, Nualart J, Ferraro G, Keim DA (2014) Task 1 of the CLEF eHealth evaluation lab 2014: Visual-Interactive Search and Exploration of eHealth Data. CLEF 2014 Working Notes
Hund M, Sturm W, Schreck T, Ullrich T, Keim D, Majnaric L, Holzinger A (2015) Analysis of patient groups and immunization results based on subspace clustering. In: Proceedings of brain informatics and health (LNCS 9250), pp 358–368
Müller E, Günnemann S, Assent I, Seidl Thomas (2009) Evaluating clustering in subspace projections of high dimensional data. Proc VLDB Endow 2(1):1270–1281
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD Explor Newslett 11(1):10–18
Shneiderman B (1996) The eyes have it: A task by data type taxonomy for information visualizations. In: Visual languages, 1996. Proceedings of IEEE symposium on, IEEE, pp 336–343
Rao R, Card SK (1994) The table lens: merging graphical and symbolic representations in an interactive focus+ context visualization for tabular information. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp 318–322
Majnarić-Trtica L, Vitale B (2011) Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination. Primary Health Care Res Dev 12(04):310–321
Trtica-Majnaric L, Zekic-Susac M, Sarlija N, Vitale B (2010) Prediction of influenza vaccination outcome by neural networks and logistic regression. J Biomed Inform 43(5):774–781
Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C,Thiel K, Wiswedel B (2007) KNIME: The Konstanz information miner. In: Studies in classification, data analysis, and knowledge organization (GfKL 2007)
Aggarwal CC, Wolf JL, Yu PS, Procopiuc C, Park JS (1999) Fast algorithms for projected clustering. In: Proceedings of ACM international conference on management of data, pp 61–72
Holzinger A (2013) Human–computer interaction and knowledge discovery (hci-kdd): what is the benefit of bringing those two fields to work together? In: Multidisciplinary research and practice for information systems (LNCS 8127), pp 319–328
Holzinger A (2014) Extravaganza tutorial on hot ideas for interactive knowledge discovery and data mining in biomedical informatics. In: Brain informatics and health (BIH) (LNAI 8609), pp 502–515
Otasek D, Pastrello C, Holzinger A, Jurisica I (2014) Visual data mining: effective exploration of the biological universe. In: Interactive knowledge discovery and data mining in biomedical informatics: state-of-the-art and future challenges. (LNCS 8401), pp 19–34
Turkay C, Jeanquartier F, Holzinger A, Hauser H (2014) On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics. In: Interactive knowledge discovery and data mining: state-of-the-art and future challenges in biomedical informatics (LNCS 8401), pp 117–140