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Task Name and Reference(s):

Out-of-Core Extension

Dimension Synthesis

Map Labeled Data

Multi-Level Mapping

Map Items Relative to Target

Name Synthesized Dimension
J.-P.Benzécri.L’analysedesdonnées,vol.2.DunodParis,1973.
A.Buja,D.Swayne,M.Littman,N.Dean,H.Hofmann,andL.Chen. Data visualization with multidimensional scaling. J. Comp. and Graph. Stat., 17(2):444–472, 2008.
D.Coimbra,R.Martins,T.Neves,A.Telea,andF.Paulovich.Explaining three-dimensional dimensionality reduction plots. Info. Vis., 15(2):154– 172, 2016.
S.Fadel,F.Fatore,F.Duarte,andF.Paulovich.Loch:Aneighborhood- based multidimensional projection technique for high-dimensional sparse spaces. Neurocomputing, 150:546–556, 2015.
N. Heulot, J. Fekete, and M. Aupetit. Visualizing dimensionality reduc- tion artifacts: An evaluation. Technical report, May 2015.

Dis. Relation Betw. Vis. Patt. & Orig. Dim.
M. Brehmer, M. Sedlmair, S. Ingram, and T. Munzner. Visualizing dimensionally-reduced data: interviews with analysts and a characteriza- tion of task sequences. In BELIV, pp. 1–8, 2014.
S. Cheng and K. Mueller. The data context map: Fusing data and at- tributes into a unified display. IEEE Trans. Vis. Comp. Graph., 22(1):121– 130, 2016.
E. Gansner, Y. Hu, and S. North. Interactive visualization of streaming text data with dynamic maps. J. Graph Alg. Appl., 17(4):515–540, 2013.
S. Ingram, T. Munzner, and M. Olano. Glimmer: Multilevel mds on the gpu. IEEE Trans. Vis. Comp. Graph., 15(2):249–261, 2009.

Discover Density-based Outlier in Data Space
N.Cao,D.Gotz,J.Sun,andH.Qu.DICON:Interactivevisualanalysisof multidimensional clusters. IEEE Trans. Vis. Comp. Graph., 17(12):2581– 2590, 2011.

Discover Neighb. of 00C It. in Data Space
H.-U.BauerandK.Pawelzik.Quantifyingtheneighborhoodpreservation of self-organizing feature maps. IEEE Trans. Neural Networks, 3(4):570– 579, 1992.

Discover Class-Outlier in Data Space
A. Barbosa, F. Paulovich, A. Paiva, S. Goldenstein, F. Petronetto, and L. Nonato. Visualizing and interacting with kernelized data. IEEE Trans. Vis. Comp. Graph., 22(3):1314–1325, 2016.
N.Cao,D.Gotz,J.Sun,andH.Qu.DICON:Interactivevisualanalysisof multidimensional clusters. IEEE Trans. Vis. Comp. Graph., 17(12):2581– 2590, 2011.

Discover Class-Outlier in Map
C. Ding and T. Li. Adaptive dimension reduction using discriminant analysis and k-means clustering. In Int. Conf. Mach. Learn., pp. 521–528, 2007.

Match Clusters and Classes in Map
J.-P.Benzécri.L’analysedesdonnées,vol.2.DunodParis,1973.
C. Bishop, M. Svensén, and C. Williams. Gtm: The generative topo- graphic mapping. Neural Computation, 10(1):215–234, 1998.
U. Brandes and C. Pich. Eigensolver methods for progressive multidi- mensional scaling of large data. In Graph Drawing, pp. 42–53, 2006.
E.T.Brown,J.Liu,C.E.Brodley,andR.Chang.Dis-function:Learning distance functions interactively. In IEEE VAST, pp. 83–92, 2012.
H.Chen,S.Zhang,W.Chen,H.Mei,J.Zhang,A.Mercer,R.Liang,,and H. Qu. Uncertainty-aware multidimensional ensemble data visualization and exploration. IEEE Trans. Vis. Comp. Graph., 2015.
L. Chen and A. Buja. Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis. J. Amer. Stat. Assoc., 104(485):209–219, 2009.
J. Choo, H. Lee, J. Kihm, and H. Park. ivisclassifier: An interactive visual analytics system for classification based on supervised dimension reduction. In IEEE VAST, pp. 27–34, 2010.
C. Ding and T. Li. Adaptive dimension reduction using discriminant analysis and k-means clustering. In Int. Conf. Mach. Learn., pp. 521–528, 2007.
S.Fadel,F.Fatore,F.Duarte,andF.Paulovich.Loch:Aneighborhood- based multidimensional projection technique for high-dimensional sparse spaces. Neurocomputing, 150:546–556, 2015.
P. Gaillard, M. Aupetit, and G. Govaert. Learning topology of a labeled data set with the supervised generative gaussian graph. Neurocomputing, 71(7-9):1283–1299, 2008.
E. Gansner, Y. Hu, and S. North. Interactive visualization of streaming text data with dynamic maps. J. Graph Alg. Appl., 17(4):515–540, 2013.
Y. Goldberg and Y. Ritov. Local procrustes for manifold embedding: a measure of embedding quality and embedding algorithms. Mach. Learn., 77(1):1–25, 2009.
E. Gomez-Nieto, F. San Roman, P. Pagliosa, W. Casaca, E. Helou, M. Oliveira, and L. Nonato. Similarity preserving snippet-based visual- ization of web search results. IEEE Trans. Vis. Comp. Graph., 20(3):457– 470, 2014.
S. Ingram, T. Munzner, and M. Olano. Glimmer: Multilevel mds on the gpu. IEEE Trans. Vis. Comp. Graph., 15(2):249–261, 2009.

Classify Out-of-Core Item in Map
C.Bishop.PatternRecognitionandMachineLearning.Springer-Verlag New York, 2016.
C. Bishop, M. Svensén, and C. Williams. Gtm: The generative topo- graphic mapping. Neural Computation, 10(1):215–234, 1998.

Map Synthesized Dim. to Orig. Dim.
J.-P.Benzécri.L’analysedesdonnées,vol.2.DunodParis,1973.
M. Brehmer, M. Sedlmair, S. Ingram, and T. Munzner. Visualizing dimensionally-reduced data: interviews with analysts and a characteriza- tion of task sequences. In BELIV, pp. 1–8, 2014.
N. Cao, J. Sun, Y.-R. Lin, D. Gotz, S. Liu, and H. Qu. Facetatlas: Multifaceted visualization for rich text corpora. IEEE Trans. Vis. Comp. Graph., 16(6):1172–1181, 2010.
J.DeLeeuwandW.Heiser.Multidimensionalscalingwithrestrictions on the configuration. Multivariate Analysis, 5:501–522, 1980.
E. Gansner, Y. Hu, and S. North. Interactive visualization of streaming text data with dynamic maps. J. Graph Alg. Appl., 17(4):515–540, 2013.
X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimen- sionality reduction for visualization and classification. IEEE Trans. Sys., Man, and Cyber., 35(6):1098–1107, 2005.
Y. Guo, T. Hastie, and R. Tibshirani. Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1):86–100, 2007.
L. House, S. Leman, and C. Han. Bayesian visual analytics: Bava. Stat. Anal. and Data Min., 8(1):1–13, 2015.

Discover Relation Betw. Orig. Dim.
J.-P.Benzécri.L’analysedesdonnées,vol.2.DunodParis,1973.
J.DeLeeuwandW.Heiser.Multidimensionalscalingwithrestrictions on the configuration. Multivariate Analysis, 5:501–522, 1980.
X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimen- sionality reduction for visualization and classification. IEEE Trans. Sys., Man, and Cyber., 35(6):1098–1107, 2005.
L. House, S. Leman, and C. Han. Bayesian visual analytics: Bava. Stat. Anal. and Data Min., 8(1):1–13, 2015.

Discover Neighbors in Map
M. Brehmer and T. Munzner. A multi-level typology of abstract vi- sualization tasks. IEEE Trans. Vis. Comp. Graph., 19(12):2376–2385, 2013.
S.Fadel,F.Fatore,F.Duarte,andF.Paulovich.Loch:Aneighborhood- based multidimensional projection technique for high-dimensional sparse spaces. Neurocomputing, 150:546–556, 2015.
E. Gomez-Nieto, F. San Roman, P. Pagliosa, W. Casaca, E. Helou, M. Oliveira, and L. Nonato. Similarity preserving snippet-based visual- ization of web search results. IEEE Trans. Vis. Comp. Graph., 20(3):457– 470, 2014.
Y. Guo, T. Hastie, and R. Tibshirani. Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1):86–100, 2007.
X. Hu, L. Bradel, D. Maiti, L. House, C. North, and S. Leman. Semantics of directly manipulating spatializations. IEEE Trans. Vis. Comp. Graph., 19(12):2052–2059, 2013.

Discover a Seed Point
J.Bertin.SemiologyofGraphics.Univ.Wisc.Press,1983.

Navigate in Map
M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality re- duction and data representation. Neural Computation, 15(6):1373–1396, 2003.
N.Cao,D.Gotz,J.Sun,andH.Qu.DICON:Interactivevisualanalysisof multidimensional clusters. IEEE Trans. Vis. Comp. Graph., 17(12):2581– 2590, 2011.

Discover a Path in Map
M. Chen and A. Golan. What may visualization processes optimize? IEEE Trans. Vis. Comp. Graph., 22(12):2619–2632, 2016.
D.Coimbra,R.Martins,T.Neves,A.Telea,andF.Paulovich.Explaining three-dimensional dimensionality reduction plots. Info. Vis., 15(2):154– 172, 2016.
C. Ding and T. Li. Adaptive dimension reduction using discriminant analysis and k-means clustering. In Int. Conf. Mach. Learn., pp. 521–528, 2007.
N. Heulot, J. Fekete, and M. Aupetit. Visualizing dimensionality reduc- tion artifacts: An evaluation. Technical report, May 2015.

Map Data with Landmarks

Brush in Data Space
M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality re- duction and data representation. Neural Computation, 15(6):1373–1396, 2003.
N.Cao,D.Gotz,J.Sun,andH.Qu.DICON:Interactivevisualanalysisof multidimensional clusters. IEEE Trans. Vis. Comp. Graph., 17(12):2581– 2590, 2011.

Navigate in Data Space
M. Aupetit and L. van der Maaten. Neurocomputing special issue on visual analytics using multidimensional projections. https:// www.sciencedirect.com/journal/neurocomputing/vol/150 (visited in May 2018).
N.Cao,D.Gotz,J.Sun,andH.Qu.DICON:Interactivevisualanalysisof multidimensional clusters. IEEE Trans. Vis. Comp. Graph., 17(12):2581– 2590, 2011.

Discover a Path in Data Space
M. Aupetit and L. van der Maaten. Neurocomputing special issue on visual analytics using multidimensional projections. https:// www.sciencedirect.com/journal/neurocomputing/vol/150 (visited in May 2018).
N.Cao,D.Gotz,J.Sun,andH.Qu.DICON:Interactivevisualanalysisof multidimensional clusters. IEEE Trans. Vis. Comp. Graph., 17(12):2581– 2590, 2011.

Discover Density-based Clusters in Data Space
M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality re- duction and data representation. Neural Computation, 15(6):1373–1396, 2003.
N.Cao,D.Gotz,J.Sun,andH.Qu.DICON:Interactivevisualanalysisof multidimensional clusters. IEEE Trans. Vis. Comp. Graph., 17(12):2581– 2590, 2011.
L. Huang, S. Matwin, E. Carvalho, and R. Minghim. Active learning with visualization for text data. In ACM Work. Explor. Search and Interac. Data Anal., pp. 69–74, 2017.

Brush in Map
J.Bertin.SemiologyofGraphics.Univ.Wisc.Press,1983.
E. Gansner, Y. Hu, and S. North. Interactive visualization of streaming text data with dynamic maps. J. Graph Alg. Appl., 17(4):515–540, 2013.
E. Gomez-Nieto, F. San Roman, P. Pagliosa, W. Casaca, E. Helou, M. Oliveira, and L. Nonato. Similarity preserving snippet-based visual- ization of web search results. IEEE Trans. Vis. Comp. Graph., 20(3):457– 470, 2014.
S. Ingram, T. Munzner, and M. Olano. Glimmer: Multilevel mds on the gpu. IEEE Trans. Vis. Comp. Graph., 15(2):249–261, 2009.

Discover Clusters in Map
J.-P.Benzécri.L’analysedesdonnées,vol.2.DunodParis,1973.
L. Chen and A. Buja. Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis. J. Amer. Stat. Assoc., 104(485):209–219, 2009.
S. Cheng and K. Mueller. The data context map: Fusing data and at- tributes into a unified display. IEEE Trans. Vis. Comp. Graph., 22(1):121– 130, 2016.
P. Gaillard, M. Aupetit, and G. Govaert. Learning topology of a labeled data set with the supervised generative gaussian graph. Neurocomputing, 71(7-9):1283–1299, 2008.
E. Gansner, Y. Hu, and S. North. Interactive visualization of streaming text data with dynamic maps. J. Graph Alg. Appl., 17(4):515–540, 2013.
E. Gomez-Nieto, F. San Roman, P. Pagliosa, W. Casaca, E. Helou, M. Oliveira, and L. Nonato. Similarity preserving snippet-based visual- ization of web search results. IEEE Trans. Vis. Comp. Graph., 20(3):457– 470, 2014.
S. Ingram, T. Munzner, and M. Olano. Glimmer: Multilevel mds on the gpu. IEEE Trans. Vis. Comp. Graph., 15(2):249–261, 2009.

Dis. Sensitiv.-based Outlier in Data Space
E.Bertini,A.Tatu,andD.Keim.Qualitymetricsinhigh-dimensional data visualization: An overview and systematization. IEEE Trans. Vis. Comp. Graph., 17(12):2203–2212, 2011.
S. Cheng and K. Mueller. The data context map: Fusing data and at- tributes into a unified display. IEEE Trans. Vis. Comp. Graph., 22(1):121– 130, 2016.

Discover Outlier in Map
Y. Bengio, J.-f. Paiement, P. Vincent, O. Delalleau, N. L. Roux, and M. Ouimet. Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. In NIPS, pp. 177–184, 2004.
M. Brehmer and T. Munzner. A multi-level typology of abstract vi- sualization tasks. IEEE Trans. Vis. Comp. Graph., 19(12):2376–2385, 2013.
Y. Guo, T. Hastie, and R. Tibshirani. Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1):86–100, 2007.

Discover Neighbors in Data Space
A. Barbosa, F. Paulovich, A. Paiva, S. Goldenstein, F. Petronetto, and L. Nonato. Visualizing and interacting with kernelized data. IEEE Trans. Vis. Comp. Graph., 22(3):1314–1325, 2016.
I.BorgandP.Groenen.Modernmultidimensionalscaling:Theoryand applications. Springer Science & Business Media, 2005.
C.FaloutsosandK.Lin.Fastmap:Afastalgorithmforindexing,datamin-ing and visualization of traditional and multimedia databases. In ACM SIGMOD, pp. 163–174, 1995.
Y. Guo, T. Hastie, and R. Tibshirani. Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1):86–100, 2007.
N. Heulot, M. Aupetit, and J. Fekete. Proxilens: Interactive exploration of high-dimensional data using projections. In EuroVis Workshop on Vis. Anal. Using Multid. Proj., 2013.

Name Cluster
J.-P.Benzécri.L’analysedesdonnées,vol.2.DunodParis,1973.
C.Bishop.PatternRecognitionandMachineLearning.Springer-Verlag New York, 2016.
S.Fadel,F.Fatore,F.Duarte,andF.Paulovich.Loch:Aneighborhood- based multidimensional projection technique for high-dimensional sparse spaces. Neurocomputing, 150:546–556, 2015.
Y. Guo, T. Hastie, and R. Tibshirani. Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1):86–100, 2007.

Sample Data Space from Map
M. Aupetit and L. van der Maaten. Neurocomputing special issue on visual analytics using multidimensional projections. https:// www.sciencedirect.com/journal/neurocomputing/vol/150 (visited in May 2018).
B. Broeksema, A. Telea, and T. Baudel. Visual analysis of multi- dimensional categorical data sets. Comp. Graph. Forum., 32(8):158–169, 2013.

Steer Projection by moving Landmarks in Map
M.Berger,K.McDonough,andL.Seversky.cite2vec:Citation-driven document exploration via word embeddings. IEEE Trans. Vis. Comp. Graph., 23(1):691–700, 2017.
E.T.Brown,J.Liu,C.E.Brodley,andR.Chang.Dis-function:Learning distance functions interactively. In IEEE VAST, pp. 83–92, 2012.
M. Chalmers. A linear iteration time layout algorithm for visualising high-dimensional data. In IEEE Visualization, pp. 127–131, 1996.
J. Choo, C. Lee, C. Reddy, and H. Park. Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Trans. Vis. Comp. Graph., 19(12):1992–2001, 2013.
D.Coimbra,R.Martins,T.Neves,A.Telea,andF.Paulovich.Explaining three-dimensional dimensionality reduction plots. Info. Vis., 15(2):154– 172, 2016.
L.DiCaro,V.Frias-Martinez,andE.Frias-Martinez.Analyzingtherole of dimension arrangement for data visualization in radviz. In Advances in Knowledge Discovery and Data Mining, pp. 125–132. Springer, 2010.
C. Ding and T. Li. Adaptive dimension reduction using discriminant analysis and k-means clustering. In Int. Conf. Mach. Learn., pp. 521–528, 2007.
F. Duarte, F. Sikansi, F. Fatore, S. Fadel, and F. Paulovich. Nmap: A novel neighborhood preservation space-filling algorithm. IEEE Trans. Vis. Comp. Graph., 20(12):2063–2071, 2014.
F.Fernández,M.Verleysen,J.Lee,andI.Blanco.Stabilitycomparison of dimensionality reduction techniques attending to data and parameter variations. In EuroVis (Short Paper), 2013.
M. Gleicher, M. Correll, C. Nothelfer, and S. Franconeri. Perception of average value in multiclass scatterplots. IEEE Trans. Vis. Comp. Graph., 19(12):2316–2325, 2013.
E. Gomez-Nieto, W. Casaca, L. G. Nonato, and G. Taubin. Mixed integer optimization for layout arrangement. In Sibgrapi, pp. 115–122, 2013.

Multidimensional Projection for Visual Analytics

Task Name and Reference(s):

Task Name and Reference(s):

Task Name and Reference(s):

Task Name and Reference(s):