LoVis: Local Pattern Visualization for Model Refinement
Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful...
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| Published in | Computer graphics forum Vol. 33; no. 3; pp. 331 - 340 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 1467-8659 |
| DOI | 10.1111/cgf.12389 |
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| Abstract | Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study. |
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| AbstractList | Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study. Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre-selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study. [PUBLICATION ABSTRACT] |
| Author | Higgins, Huong N. Ward, Matthew O. Zhao, Kaiyu Rundensteiner, Elke A. |
| Author_xml | – sequence: 1 givenname: Kaiyu surname: Zhao fullname: Zhao, Kaiyu organization: Worcester Polytechnic Institute, Worcester, MA 01760 – sequence: 2 givenname: Matthew O. surname: Ward fullname: Ward, Matthew O. organization: Worcester Polytechnic Institute, Worcester, MA 01760 – sequence: 3 givenname: Elke A. surname: Rundensteiner fullname: Rundensteiner, Elke A. organization: Worcester Polytechnic Institute, Worcester, MA 01760 – sequence: 4 givenname: Huong N. surname: Higgins fullname: Higgins, Huong N. organization: Worcester Polytechnic Institute, Worcester, MA 01760 |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| References | Wu Y., Gaunt C., Gray S.: A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics 6, 1 (2010), 34-45. 4 Huber P.J.: Robust statistics. Springer, Berlin Heidelberg, 2011. 5 R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2012. ISBN 3-900051-07-0. URL: http://www.R-project.org/. 1 Kaufman L., Rousseeuw P.J.: Finding groups in data: an introduction to cluster analysis, vol. 344. John Wiley & Sons, 2009. 7 Guyon I., Elisseeff A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3 (2003), 1157-1182. 1 Cook R.D., Weisberg S.: Residuals and influence in regression, vol. 5. Chapman and Hall New York, 1982. 6 Kruskal J.B., Landwehr J.M.: Icicle plots: Better displays for hierarchical clustering. The American Statistician 37, 2 (1983), 162-168. 7 McGuffin M.J., Robert J.-M.: Quantifying the space-efficiency of 2d graphical representations of trees. Information Visualization 9, 2 (2010), 115-140. 7 Elmqvist N., Fekete J.-D.: Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines. IEEE Transactions on Visualization and Computer Graphics 16, 3 (2010), 439-454. 7 Brown G., Wyatt J., Harris R., Yao X.: Diversity creation methods: a survey and categorisation. Information Fusion 6, 1 (2005), 5-20. 2 Muhlbacher T., Piringer H.: A partition-based framework for building and validating regression models. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 1962-1971. 1, 2, 4, 5 Tukey J.W.: Exploratory data analysis. Addison-Wesley, Reading, Massachusetts, 1977. 4 Bickel P.J., Hammel E.A., OŠConnell J.W., et al.: Sex bias in graduate admissions: Data from berkeley. Science 187, 4175 (1975), 398-404. 1, 2 Reshef D.N., Reshef Y.A., Finucane H.K., Grossman S.R., McVean G., Turnbaugh P.J., Lander E.S., Mitzenmacher M., Sabeti P.C.: Detecting novel associations in large data sets. Science 334, 6062 (2011), 1518-1524. 2 Johansson S., Johansson J.: Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transactions on Visualization and Computer Graphics 15, 6 (2009), 993-1000. 1, 2 Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11, 1 (2009), 10-18. 1 Kosara R., Bendix F., Hauser H.: Parallel sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualization and Computer Graphics 12, 4 (2006), 558-568. 9 2013; 19 2009; 11 2011; 334 2010; 16 2012 2006; 12 1990 2011 2000 1982; 5 2009 2003; 3 2008 2005; 6 2009; 344 2005 2004 1975; 187 2013 1983; 37 2009; 15 2010; 6 2010; 9 1977 e_1_2_7_5_2 e_1_2_7_4_2 e_1_2_7_3_2 e_1_2_7_2_2 e_1_2_7_9_2 e_1_2_7_8_2 e_1_2_7_7_2 e_1_2_7_6_2 e_1_2_7_19_2 e_1_2_7_18_2 e_1_2_7_17_2 e_1_2_7_14_2 R Core Team (e_1_2_7_24_2) 2012 e_1_2_7_13_2 e_1_2_7_12_2 e_1_2_7_10_2 e_1_2_7_26_2 e_1_2_7_27_2 Tukey J.W. (e_1_2_7_30_2) 1977 e_1_2_7_29_2 Talbot J. (e_1_2_7_28_2) 2009 Havre S. (e_1_2_7_11_2) 2000 Kaufman L. (e_1_2_7_16_2) 2009 e_1_2_7_25_2 e_1_2_7_23_2 e_1_2_7_31_2 e_1_2_7_22_2 e_1_2_7_32_2 e_1_2_7_21_2 e_1_2_7_33_2 e_1_2_7_20_2 Kruskal J.B. (e_1_2_7_15_2) 1983; 37 |
| References_xml | – reference: Kruskal J.B., Landwehr J.M.: Icicle plots: Better displays for hierarchical clustering. The American Statistician 37, 2 (1983), 162-168. 7 – reference: Reshef D.N., Reshef Y.A., Finucane H.K., Grossman S.R., McVean G., Turnbaugh P.J., Lander E.S., Mitzenmacher M., Sabeti P.C.: Detecting novel associations in large data sets. Science 334, 6062 (2011), 1518-1524. 2 – reference: Tukey J.W.: Exploratory data analysis. Addison-Wesley, Reading, Massachusetts, 1977. 4 – reference: McGuffin M.J., Robert J.-M.: Quantifying the space-efficiency of 2d graphical representations of trees. Information Visualization 9, 2 (2010), 115-140. 7 – reference: Muhlbacher T., Piringer H.: A partition-based framework for building and validating regression models. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 1962-1971. 1, 2, 4, 5 – reference: Johansson S., Johansson J.: Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transactions on Visualization and Computer Graphics 15, 6 (2009), 993-1000. 1, 2 – reference: Brown G., Wyatt J., Harris R., Yao X.: Diversity creation methods: a survey and categorisation. Information Fusion 6, 1 (2005), 5-20. 2 – reference: Bickel P.J., Hammel E.A., OŠConnell J.W., et al.: Sex bias in graduate admissions: Data from berkeley. Science 187, 4175 (1975), 398-404. 1, 2 – reference: Kosara R., Bendix F., Hauser H.: Parallel sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualization and Computer Graphics 12, 4 (2006), 558-568. 9 – reference: R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2012. ISBN 3-900051-07-0. URL: http://www.R-project.org/. 1 – reference: Wu Y., Gaunt C., Gray S.: A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics 6, 1 (2010), 34-45. 4 – reference: Huber P.J.: Robust statistics. Springer, Berlin Heidelberg, 2011. 5 – reference: Elmqvist N., Fekete J.-D.: Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines. IEEE Transactions on Visualization and Computer Graphics 16, 3 (2010), 439-454. 7 – reference: Guyon I., Elisseeff A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3 (2003), 1157-1182. 1 – reference: Kaufman L., Rousseeuw P.J.: Finding groups in data: an introduction to cluster analysis, vol. 344. John Wiley & Sons, 2009. 7 – reference: Cook R.D., Weisberg S.: Residuals and influence in regression, vol. 5. Chapman and Hall New York, 1982. 6 – reference: Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11, 1 (2009), 10-18. 1 – volume: 5 year: 1982 – year: 2011 – start-page: 230 year: 1990 end-page: 237 – start-page: 352 year: 2012 end-page: 360 – start-page: 129 year: 2011 end-page: 138 – volume: 37 start-page: 162 issue: 2 year: 1983 end-page: 168 article-title: Icicle plots: Better displays for hierarchical clustering publication-title: The American Statistician – start-page: 63 year: 2012 end-page: 72 – volume: 187 start-page: 398 issue: 4175 year: 1975 end-page: 404 article-title: Sex bias in graduate admissions: Data from berkeley publication-title: Science – start-page: 1283 year: 2009 end-page: 1292 – start-page: 111 year: 2011 end-page: 120 – year: 1977 – volume: 15 start-page: 993 issue: 6 year: 2009 end-page: 1000 article-title: Interactive dimensionality reduction through user‐defined combinations of quality metrics publication-title: IEEE Transactions on Visualization and Computer Graphics – volume: 19 start-page: 1962 issue: 12 year: 2013 end-page: 1971 article-title: A partition‐based framework for building and validating regression models publication-title: IEEE Transactions on Visualization and Computer Graphics – start-page: 65 year: 2004 end-page: 72 – year: 2012 – start-page: 89 year: 2004 end-page: 96 – volume: 344 year: 2009 – start-page: 75 year: 2009 end-page: 82 – start-page: 115 year: 2000 end-page: 123 – volume: 9 start-page: 115 issue: 2 year: 2010 end-page: 140 article-title: Quantifying the space‐efficiency of 2d graphical representations of trees publication-title: Information Visualization – volume: 12 start-page: 558 issue: 4 year: 2006 end-page: 568 article-title: Parallel sets: Interactive exploration and visual analysis of categorical data publication-title: IEEE Transactions on Visualization and Computer Graphics – start-page: 240 year: 2008 end-page: 245 – volume: 6 start-page: 34 issue: 1 year: 2010 end-page: 45 article-title: A comparison of alternative bankruptcy prediction models publication-title: Journal of Contemporary Accounting & Economics – volume: 3 start-page: 1157 year: 2003 end-page: 1182 article-title: An introduction to variable and feature selection publication-title: The Journal of Machine Learning Research – volume: 11 start-page: 10 issue: 1 year: 2009 end-page: 18 article-title: The weka data mining software: an update publication-title: ACM SIGKDD Explorations Newsletter – volume: 334 start-page: 1518 issue: 6062 year: 2011 end-page: 1524 article-title: Detecting novel associations in large data sets publication-title: Science – volume: 6 start-page: 5 issue: 1 year: 2005 end-page: 20 article-title: Diversity creation methods: a survey and categorisation publication-title: Information Fusion – start-page: 73 year: 2004 end-page: 80 – start-page: 157 year: 2005 end-page: 164 – volume: 16 start-page: 439 issue: 3 year: 2010 end-page: 454 article-title: Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines publication-title: IEEE Transactions on Visualization and Computer Graphics – year: 2013 – ident: e_1_2_7_8_2 doi: 10.1109/VAST.2009.5333431 – ident: e_1_2_7_29_2 doi: 10.1109/VAST.2012.6400488 – volume-title: Exploratory data analysis year: 1977 ident: e_1_2_7_30_2 – ident: e_1_2_7_10_2 doi: 10.1145/1656274.1656278 – ident: e_1_2_7_13_2 doi: 10.1109/TVCG.2009.153 – start-page: 115 volume-title: IEEE Symposium on Information Visualization year: 2000 ident: e_1_2_7_11_2 – ident: e_1_2_7_27_2 – ident: e_1_2_7_18_2 doi: 10.1109/VAST.2011.6102448 – ident: e_1_2_7_31_2 doi: 10.1109/INFVIS.2005.1532142 – volume: 37 start-page: 162 issue: 2 year: 1983 ident: e_1_2_7_15_2 article-title: Icicle plots: Better displays for hierarchical clustering publication-title: The American Statistician doi: 10.1080/00031305.1983.10482733 – ident: e_1_2_7_26_2 – ident: e_1_2_7_7_2 doi: 10.1145/2339530.2339588 – ident: e_1_2_7_21_2 doi: 10.1109/IV.2008.17 – volume-title: Finding groups in data: an introduction to cluster analysis year: 2009 ident: e_1_2_7_16_2 – ident: e_1_2_7_3_2 doi: 10.1016/j.inffus.2004.04.004 – ident: e_1_2_7_23_2 – ident: e_1_2_7_19_2 doi: 10.1109/TVCG.2013.125 – ident: e_1_2_7_6_2 doi: 10.1162/153244303322753616 – ident: e_1_2_7_17_2 doi: 10.1109/VISUAL.1990.146386 – ident: e_1_2_7_2_2 doi: 10.1126/science.187.4175.398 – ident: e_1_2_7_32_2 doi: 10.1016/j.jcae.2010.04.002 – ident: e_1_2_7_12_2 doi: 10.1007/978-3-642-04898-2_594 – ident: e_1_2_7_9_2 doi: 10.1109/VAST.2011.6102450 – ident: e_1_2_7_25_2 doi: 10.1126/science.1205438 – ident: e_1_2_7_5_2 doi: 10.1109/TVCG.2009.84 – ident: e_1_2_7_22_2 – ident: e_1_2_7_4_2 doi: 10.1007/978-1-4612-4444-8_1 – ident: e_1_2_7_14_2 doi: 10.1109/TVCG.2006.76 – ident: e_1_2_7_33_2 – volume-title: R: A Language and Environment for Statistical Computing year: 2012 ident: e_1_2_7_24_2 – start-page: 1283 volume-title: Proceedings of the 27th international conference on Human factors in computing systems year: 2009 ident: 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| SubjectTerms | Analysis Categories and Subject Descriptors (according to ACM CCS) Construction Data points Generalized linear models H.5.2 [Information Interfaces and Presentation]: User Interfaces-Graphical user interfaces (GUI) Mathematical models Pattern recognition Representations Studies Tasks Trends Visual Visualization |
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| Title | LoVis: Local Pattern Visualization for Model Refinement |
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