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Recent research advances on interactive machine learning

  • Liu Jiang
  • Shixia LiuEmail author
  • Changjian Chen
Regular Paper

Abstract

Interactive machine learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.

Graphical abstract

Keywords

Interactive visualization Machine learning Interactive machine learning 

References

  1. Alexander E, Gleicher M (2016) Task-driven comparison of topic models. IEEE Trans Vis Comput Graph 22(1):320–329CrossRefGoogle Scholar
  2. Andrienko G, Andrienko N, Fuchs G, Garcia JMC (2018) Clustering trajectories by relevant parts for air traffic analysis. IEEE Trans Vis Comput Graph 24(1):34–44CrossRefGoogle Scholar
  3. Badam SK, Elmqvist N, Fekete JD (2017) Steering the craft: UI elements and visualizations for supporting progressive visual analytics. Comput Graph Forum 36(3):491–502CrossRefGoogle Scholar
  4. Barbosa A, Paulovich FV, Paiva A, Goldenstein S, Petronetto F, Nonato LG (2016) Visualizing and interacting with kernelized data. IEEE Trans Vis Comput Graph 22(3):1314–1325CrossRefGoogle Scholar
  5. Behrisch M, Bach B, Hund M, Delz M, Von Rüden L, Fekete JD, Schreck T (2017) Magnostics: image-based search of interesting matrix views for guided network exploration. IEEE Trans Vis Comput Graph 23(1):31–40CrossRefGoogle Scholar
  6. Berger M, McDonough K, Seversky LM (2017) cite2vec: citation-driven document exploration via word embeddings. IEEE Trans Vis Comput Graph 23(1):691–700CrossRefGoogle Scholar
  7. Bernard J, Hutter M, Zeppelzauer M, Fellner D, Sedlmair M (2018) Comparing visual-interactive labeling with active learning: an experimental study. IEEE Trans Vis Comput Graph 24(1):298–308CrossRefGoogle Scholar
  8. Bilal A, Jourabloo A, Ye M, Liu X, Ren L (2018) Do convolutional neural networks learn class hierarchy? IEEE Trans Vis Comput Graph 24(1):152–162CrossRefGoogle Scholar
  9. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. JMLR 3(Jan):993–1022zbMATHGoogle Scholar
  10. Bögl M, Filzmoser P, Gschwandtner T, Lammarsch T, Leite RA, Miksch S, Rind A (2017) Cycle plot revisited: multivariate outlier detection using a distance-based abstraction. Comput Graph Forum 36(3):227–238CrossRefGoogle Scholar
  11. Brooks M, Amershi S, Lee B, Drucker SM, Kapoor A, Simard P (2015) FeatureInsight: visual support for error-driven feature ideation in text classification. In: IEEE VAST, pp 105–112Google Scholar
  12. Bryan C, Wu X, Mniszewski S, Ma KL (2015) Integrating predictive analytics into a spatiotemporal epidemic simulation. IEEE VAST:17–24Google Scholar
  13. Buchmüller J, Janetzko H, Andrienko G, Andrienko N, Fuchs G, Keim DA (2015) Visual analytics for exploring local impact of air traffic. Comput Graph Forum 34(3):181–190CrossRefGoogle Scholar
  14. Cao N, Shi C, Lin S, Lu J, Lin YR, Lin CY (2016) TargetVue: visual analysis of anomalous user behaviors in online communication systems. IEEE Trans Vis Comput Graph 22(1):280–289CrossRefGoogle Scholar
  15. Cao N, Lin C, Zhu Q, Lin YR, Teng X, Wen X (2018) Voila: visual anomaly detection and monitoring with streaming spatiotemporal data. IEEE Trans Vis Comput Graph 24(1):23–33CrossRefGoogle Scholar
  16. Chen J, Zhu J, Wang Z, Zheng X, Zhang B (2013) Scalable inference for logistic-normal topic models. In: NIPS, pp 2445–2453Google Scholar
  17. Chen S, Yuan X, Wang Z, Guo C, Liang J, Wang Z, Zhang XL, Zhang J (2016) Interactive visual discovering of movement patterns from sparsely sampled geo-tagged social media data. IEEE Trans Vis Comput Graph 22(1):270–279CrossRefGoogle Scholar
  18. Chen Y, Xu P, Ren L (2018) Sequence synopsis: optimize visual summary of temporal event data. IEEE Trans Vis Comput Graph 24(1):45–55CrossRefGoogle Scholar
  19. Cheng S, Mueller K (2016) The data context map: fusing data and attributes into a unified display. IEEE Trans Vis Comput Graph 22(1):121–130CrossRefGoogle Scholar
  20. Cho I, Wesslen R, Volkova S, Ribarsky W, Dou W (2017) CrystalBall—a visual analytic system for future event discovery and analysis from social media data. In: IEEE VASTGoogle Scholar
  21. Choo J, Liu S (2018) Visual analytics for explainable deep learning. IEEE Comput Graph Appl 38(4):84–92CrossRefGoogle Scholar
  22. Di Lorenzo G, Sbodio M, Calabrese F, Berlingerio M, Pinelli F, Nair R (2016) AllAboard: visual exploration of cellphone mobility data to optimise public transport. IEEE Trans Vis Comput Graph 22(2):1036–1050CrossRefGoogle Scholar
  23. Dou W, Liu S (2016) Topic-and time-oriented visual text analysis. IEEE Comput Graph Appl 36(4):8–13CrossRefGoogle Scholar
  24. Dou W, Cho I, ElTayeby O, Choo J, Wang X, Ribarsky W (2015) DemographicVis: analyzing demographic information based on user generated content. In: IEEE VAST, pp 57–64Google Scholar
  25. Duan Y, Chen Z, Wei F, Zhou M, Shum HY (2012) Twitter topic summarization by ranking tweets using social influence and content quality. In: COLING, pp 763–780Google Scholar
  26. El-Assady M, Gold V, Acevedo C, Collins C, Keim D (2016) ConToVi: multi-party conversation exploration using topic-space views. Comput Graph Forum 35(3):431–440CrossRefGoogle Scholar
  27. El-Assady M, Sevastjanova R, Gipp B, Keim D, Collins C (2017) NEREx: named-entity relationship exploration in multi-party conversations. Comput Graph Forum 36(3):213–225CrossRefGoogle Scholar
  28. El-Assady M, Sevastjanova R, Sperrle F, Keim D, Collins C (2018) Progressive learning of topic modeling parameters: a visual analytics framework. IEEE Trans Vis Comput Graph 24(1):382–391CrossRefGoogle Scholar
  29. Endert A, Ribarsky W, Turkay C, Wong B, Nabney I, Blanco ID, Rossi F (2017) The state of the art in integrating machine learning into visual analytics. Comput Graph Forum 36(8):458–486CrossRefGoogle Scholar
  30. Fails JA, Olsen Jr DR (2003) Interactive machine learning. In: ACM IUI, pp 39–45Google Scholar
  31. Gad S, Javed W, Ghani S, Elmqvist N, Ewing T, Hampton KN, Ramakrishnan N (2015) ThemeDelta: dynamic segmentations over temporal topic models. IEEE Trans Vis Comput Graph 21(5):672–685CrossRefGoogle Scholar
  32. Glueck M, Naeini MP, Doshi-Velez F, Chevalier F, Khan A, Wigdor D, Brudno M (2018) PhenoLines: phenotype comparison visualizations for disease subtyping via topic models. IEEE Trans Vis Comput Graph 24(1):371–381CrossRefGoogle Scholar
  33. Grünwald PD (2007) The minimum description length principle. MIT Press, CambridgeGoogle Scholar
  34. Guo S, Xu K, Zhao R, Gotz D, Zha H, Cao N (2018) EventThread: visual summarization and stage analysis of event sequence data. IEEE Trans Vis Comput Graph 24(1):56–65CrossRefGoogle Scholar
  35. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, AmsterdamzbMATHGoogle Scholar
  36. Heimerl F, Han Q, Koch S, Ertl T (2016) CiteRivers: visual analytics of citation patterns. IEEE Trans Vis Comput Graph 22(1):190–199CrossRefGoogle Scholar
  37. Hohman FM, Kahng M, Pienta R, Chau DH (2018) Visual analytics in deep learning: an interrogative survey for the next frontiers. IEEE Trans Vis Comput Graph.  https://doi.org/10.1109/TVCG.2018.2843369 CrossRefGoogle Scholar
  38. Jäckle D, Fischer F, Schreck T, Keim DA (2016) Temporal MDS plots for analysis of multivariate data. IEEE Trans Vis Comput Graph 22(1):141–150CrossRefGoogle Scholar
  39. Jäckle D, Hund M, Behrisch M, Keim DA, Schreck T (2017) Pattern trails: visual analysis of pattern transitions in subspaces. In: IEEE VASTGoogle Scholar
  40. Jarema M, Demir I, Kehrer J, Westermann R (2015) Comparative visual analysis of vector field ensembles. IEEE VAST:81–88Google Scholar
  41. Jiang X, Zhang J (2016) A text visualization method for cross-domain research topic mining. J Vis 19(3):561–576CrossRefGoogle Scholar
  42. Joachims T (2002) Optimizing search engines using clickthrough data. In: SIGKDD, pp 133–142Google Scholar
  43. Kahng M, Andrews PY, Kalro A, Chau DHP (2018) ActiVis: visual exploration of industry-scale deep neural network models. IEEE Trans Vis Comput Graph 24(1):88–97CrossRefGoogle Scholar
  44. Kim M, Kang K, Park D, Choo J, Elmqvist N (2017) TopicLens: efficient multi-level visual topic exploration of large-scale document collections. IEEE Trans Vis Comput Graph 23(1):151–160CrossRefGoogle Scholar
  45. Klemm P, Lawonn K, Glaßer S, Niemann U, Hegenscheid K, Völzke H, Preim B (2016) 3D regression heat map analysis of population study data. IEEE Trans Vis Comput Graph 22(1):81–90CrossRefGoogle Scholar
  46. Krause J, Dasgupta A, Swartz J, Aphinyanaphongs Y, Bertini E (2017) A workflow for visual diagnostics of binary classifiers using instance-level explanations. IEEE VASTGoogle Scholar
  47. Krueger R, Thom D, Ertl T (2015) Semantic enrichment of movement behavior with foursquare—a visual analytics approach. IEEE Trans Vis Comput Graph 21(8):903–915CrossRefGoogle Scholar
  48. Kumpf A, Tost B, Baumgart M, Riemer M, Westermann R, Rautenhaus M (2018) Visualizing confidence in cluster-based ensemble weather forecast analyses. IEEE Trans Vis Comput Graph 24(1):109–119CrossRefGoogle Scholar
  49. Kwon BC, Kim H, Wall E, Choo J, Park H, Endert A (2017) AxiSketcher: interactive nonlinear axis mapping of visualizations through user drawings. IEEE Trans Vis Comput Graph 23(1):221–230CrossRefGoogle Scholar
  50. Kwon BC, Eysenbach B, Verma J, Ng K, De Filippi C, Stewart WF, Perer A (2018) Clustervision: visual supervision of unsupervised clustering. IEEE Trans Vis Comput Graph 24(1):142–151CrossRefGoogle Scholar
  51. Lei H, Xia J, Guo F, Zou Y, Chen W, Liu Z (2016) Visual exploration of latent ranking evolutions in time series. J Vis 19(4):783–795CrossRefGoogle Scholar
  52. Leite RA, Gschwandtner T, Miksch S, Kriglstein S, Pohl M, Gstrein E, Kuntner J (2018) EVA: visual analytics to identify fraudulent events. IEEE Trans Vis Comput Graph 24(1):330–339CrossRefGoogle Scholar
  53. Liang Y, Wang X, Zhang SH, Hu SM, Liu S (2017) PhotoRecomposer: interactive photo recomposition by cropping. IEEE Trans Vis Comput Graph 24(10):2728–2742CrossRefGoogle Scholar
  54. Lin H, Gao S, Gotz D, Du F, He J, Cao N (2018) RCLens: interactive rare category exploration and identification. IEEE Trans Vis Comput Graph 24:2223–2237.  https://doi.org/10.1109/TVCG.2017.2711030 CrossRefGoogle Scholar
  55. Liu S, Cui W, Wu Y, Liu M (2014) A survey on information visualization: recent advances and challenges. Vis Comput 30(12):1373–1393CrossRefGoogle Scholar
  56. Liu S, Wang B, Thiagarajan JJ, Bremer PT, Pascucci V (2015) Visual exploration of high-dimensional data through subspace analysis and dynamic projections. Comput Graph Forum 34(3):271–280CrossRefGoogle Scholar
  57. Liu M, Liu S, Zhu X, Liao Q, Wei F, Pan S (2016a) An uncertainty-aware approach for exploratory microblog retrieval. IEEE Trans Vis Comput Graph 22(1):250–259CrossRefGoogle Scholar
  58. Liu S, Bremer PT, Jayaraman J, Wang B, Summa B, Pascucci V (2016b) The Grassmannian Atlas: a general framework for exploring linear projections of high-dimensional data. Comput Graph Forum 35(3):1–10CrossRefGoogle Scholar
  59. Liu S, Yin J, Wang X, Cui W, Cao K, Pei J (2016c) Online visual analytics of text streams. IEEE Trans Vis Comput Graph 22(11):2451–2466CrossRefGoogle Scholar
  60. Liu M, Jiang L, Liu J, Wang X, Zhu J, Liu S (2017a) Improving learning-from-crowds through expert validation. In: IJCAI, pp 2329–2336Google Scholar
  61. Liu M, Shi J, Li Z, Li C, Zhu J, Liu S (2017b) Towards better analysis of deep convolutional neural networks. IEEE Trans Vis Comput Graph 23(1):91–100CrossRefGoogle Scholar
  62. Liu S, Wang X, Liu M, Zhu J (2017c) Towards better analysis of machine learning models: a visual analytics perspective. Vis Inf 1(1):48–56Google Scholar
  63. Liu Z, Kerr B, Dontcheva M, Grover J, Hoffman M, Wilson A (2017d) CoreFlow: extracting and visualizing branching patterns from event sequences. Comput Graph Forum 36(3):527–538CrossRefGoogle Scholar
  64. Liu Z, Wang Y, Dontcheva M, Hoffman M, Walker S, Wilson A (2017e) Patterns and sequences: interactive exploration of clickstreams to understand common visitor paths. IEEE Trans Vis Comput Graph 23(1):321–330CrossRefGoogle Scholar
  65. Liu M, Liu S, Su H, Cao K, Zhu J (2018a) Analyzing the noise robustness of deep neural networks. In: IEEE VASTGoogle Scholar
  66. Liu M, Shi J, Cao K, Zhu J, Liu S (2018b) Analyzing the training processes of deep generative models. IEEE Trans Vis Comput Graph 24(1):77–87CrossRefGoogle Scholar
  67. Liu S, Bremer PT, Thiagarajan JJ, Srikumar V, Wang B, Livnat Y, Pascucci V (2018c) Visual exploration of semantic relationships in neural word embeddings. IEEE Trans Vis Comput Graph 24(1):553–562CrossRefGoogle Scholar
  68. Liu S, Chen C, Lu Y, Ouyang F, Wang B (2018d) An interactive method to improve crowdsourced annotations. IEEE Trans Vis Comput Graph.  https://doi.org/10.1109/TVCG.2018.2864843 CrossRefGoogle Scholar
  69. Liu S, Wang X, Collins C, Dou W, Ouyang F, El-Assady M, Jiang L, Keim D (2018e) Bridging text visualization and mining: a task-driven survey. IEEE Trans Vis Comput Graph.  https://doi.org/10.1109/TVCG.2018.2834341 CrossRefGoogle Scholar
  70. Liu S, Xiao J, Liu J, Wang X, Wu J, Zhu J (2018f) Visual diagnosis of tree boosting methods. IEEE Trans Vis Comput Graph 24(1):163–173CrossRefGoogle Scholar
  71. Löwe T, Förster EC, Albuquerque G, Kreiss JP, Magnor M (2016) Visual analytics for development and evaluation of order selection criteria for autoregressive processes. IEEE Trans Vis Comput Graph 22(1):151–159CrossRefGoogle Scholar
  72. Lu M, Liang J, Wang Z, Yuan X (2016) Exploring OD patterns of interested region based on taxi trajectories. J Vis 19(4):811–821CrossRefGoogle Scholar
  73. Lu M, Chen S, Lai C, Lin L, Yuan X (2017a) Frontier of information visualization and visual analytics in 2016. J Vis 20(4):667–686CrossRefGoogle Scholar
  74. Lu Y, Garcia R, Hansen B, Gleicher M, Maciejewski R (2017b) The state-of-the-art in predictive visual analytics. Comput Graph Forum 36(3):539–562CrossRefGoogle Scholar
  75. Lu Y, Wang H, Landis S, Maciejewski R (2018) A visual analytics framework for identifying topic drivers in media events. IEEE Trans Vis Comput Graph. 24(9):2501–2515CrossRefGoogle Scholar
  76. Ming Y, Cao S, Zhang R, Li Z, Chen Y, Song Y, Qu H (2017) Understanding hidden memories of recurrent neural networks. In: IEEE VASTGoogle Scholar
  77. Mühlbacher T, Linhardt L, Möller T, Piringer H (2018) TreePOD: sensitivity-aware selection of Pareto-optimal decision trees. IEEE Trans Vis Comput Graph 24(1):174–183CrossRefGoogle Scholar
  78. Onoue Y, Koyamada K (2017) Quasi-biclique edge concentration: a visual analytics method for biclustering. In: IEEE PacificVis, pp 215–219Google Scholar
  79. Paiva JGS, Schwartz WR, Pedrini H, Minghim R (2015) An approach to supporting incremental visual data classification. IEEE Trans Vis Comput Graph 21(1):4–17CrossRefGoogle Scholar
  80. Pajer S, Streit M, Torsney-Weir T, Spechtenhauser F, Möller T, Piringer H (2017) WeightLifter: visual weight space exploration for multi-criteria decision making. IEEE Trans Vis Comput Graph 23(1):611–620CrossRefGoogle Scholar
  81. Park D, Kim S, Lee J, Choo J, Diakopoulos N, Elmqvist N (2018) ConceptVector: text visual analytics via interactive lexicon building using word embedding. IEEE Trans Vis Comput Graph 24(1):361–370CrossRefGoogle Scholar
  82. Pezzotti N, Höllt T, Lelieveldt B, Eisemann E, Vilanova A (2016) Hierarchical stochastic neighbor embedding. Comput Graph Forum 35(3):21–30CrossRefGoogle Scholar
  83. Pezzotti N, Lelieveldt BP, van der Maaten L, Höllt T, Eisemann E, Vilanova A (2017) Approximated and user steerable tSNE for progressive visual analytics. IEEE Trans Vis Comput Graph 23(7):1739–1752CrossRefGoogle Scholar
  84. Pezzotti N, Höllt T, Van Gemert J, Lelieveldt BP, Eisemann E, Vilanova A (2018) DeepEyes: progressive visual analytics for designing deep neural networks. IEEE Trans Vis Comput Graph 24(1):98–108CrossRefGoogle Scholar
  85. Poco J, Doraiswamy H, Vo H, Comba JL, Freire J, Silva C et al (2015) Exploring traffic dynamics in urban environments using vector-valued functions. Comput Graph Forum 34(3):161–170CrossRefGoogle Scholar
  86. Pu Y, Gan Z, Henao R, Yuan X, Li C, Stevens A, Carin L (2016) Variational autoencoder for deep learning of images, labels and captions. In: NIPS, pp 2352–2360Google Scholar
  87. Purwantiningsih O, Sallaberry A, Andary S, Seilles A, Azé J (2016) Visual analysis of body movement in serious games for healthcare. In: IEEE PacificVis, pp 229–233Google Scholar
  88. Raidou RG, Casares-Magaz O, Muren L, Van der Heide UA, Rørvik J, Breeuwer M, Vilanova A (2016) Visual analysis of tumor control models for prediction of radiotherapy response. Comput Graph Forum 35(3):231–240CrossRefGoogle Scholar
  89. Rauber PE, Fadel SG, Falcao AX, Telea AC (2017) Visualizing the hidden activity of artificial neural networks. IEEE Trans Vis Comput Graph 23(1):101–110CrossRefGoogle Scholar
  90. Ren D, Amershi S, Lee B, Suh J, Williams JD (2017) Squares: supporting interactive performance analysis for multiclass classifiers. IEEE Trans Vis Comput Graph 23(1):61–70CrossRefGoogle Scholar
  91. Rieck B, Leitte H (2016) Exploring and comparing clusterings of multivariate data sets using persistent homology. Comput Graph Forum 35(3):81–90CrossRefGoogle Scholar
  92. Röhlig M, Luboschik M, Krüger F, Kirste T, Schumann H, Bögl M, Alsallakh B, Miksch S (2015) Supporting activity recognition by visual analytics. In: IEEE VAST, pp 41–48Google Scholar
  93. Sacha D, Zhang L, Sedlmair M, Lee JA, Peltonen J, Weiskopf D, North SC, Keim DA (2017) Visual interaction with dimensionality reduction: a structured literature analysis. IEEE Trans Vis Comput Graph 23(1):241–250CrossRefGoogle Scholar
  94. Sacha D, Kraus M, Bernard J, Behrisch M, Schreck T, Asano Y, Keim DA (2018) SOMFlow: guided exploratory cluster analysis with self-organizing maps and analytic provenance. IEEE Trans Vis Comput Graph 24(1):120–130CrossRefGoogle Scholar
  95. Shao L, Mahajan A, Schreck T, Lehmann DJ (2017) Interactive regression lens for exploring scatter plots. Comput Graph Forum 36(3):157–166CrossRefGoogle Scholar
  96. Stahnke J, Dörk M, Müller B, Thom A (2016) Probing projections: interaction techniques for interpreting arrangements and errors of dimensionality reductions. IEEE Trans Vis Comput Graph 22(1):629–638CrossRefGoogle Scholar
  97. Strobelt H, Gehrmann S, Pfister H, Rush AM (2018) LSTMVis: a tool for visual analysis of hidden state dynamics in recurrent neural networks. IEEE Trans Vis Comput Graph 24(1):667–676CrossRefGoogle Scholar
  98. Sun M, Mi P, North C, Ramakrishnan N (2016) BiSet: semantic edge bundling with biclusters for sensemaking. IEEE Trans Vis Comput Graph 22(1):310–319CrossRefGoogle Scholar
  99. Thom D, Krüger R, Ertl T, Bechstedt U, Platz A, Zisgen J, Volland B (2015) Can twitter really save your life? A case study of visual social media analytics for situation awareness. In: IEEE PacificVis, pp 183–190Google Scholar
  100. Turkay C, Kaya E, Balcisoy S, Hauser H (2017) Designing progressive and interactive analytics processes for high-dimensional data analysis. IEEE Trans Vis Comput Graph 23(1):131–140CrossRefGoogle Scholar
  101. Verma J, Luo H, Hu J, Zhang P (2017) DrugPathSeeker: interactive UI for exploring drug-ADR relation via pathways. In: IEEE PacificVis, pp 260–264Google Scholar
  102. Wall E, Das S, Chawla R, Kalidindi B, Brown ET, Endert A (2018) Podium: ranking data using mixed-initiative visual analytics. IEEE Trans Vis Comput Graph 24(1):288–297CrossRefGoogle Scholar
  103. Wang J, Mueller K (2016) The visual causality analyst: an interactive interface for causal reasoning. IEEE Trans Vis Comput Graph 22(1):230–239CrossRefGoogle Scholar
  104. Wang B, Mueller K (2018) The subspace voyager: exploring high-dimensional data along a continuum of salient 3D subspaces. IEEE Trans Vis Comput Graph 24(2):1204–1222CrossRefGoogle Scholar
  105. Wang X, Liu S, Chen Y, Peng TQ, Su J, Yang J, Guo B (2016a) How ideas flow across multiple social groups. In: IEEE VAST, pp 51–60Google Scholar
  106. Wang X, Liu S, Liu J, Chen J, Zhu J, Guo B (2016b) TopicPanorama: a full picture of relevant topics. IEEE Trans Vis Comput Graph 22(12):2508–2521CrossRefGoogle Scholar
  107. Wang Y, Li J, Nie F, Theisel H, Gong M, Lehmann DJ (2017) Linear discriminative star coordinates for exploring class and cluster separation of high dimensional data. Comput Graph Forum 36(3):401–410CrossRefGoogle Scholar
  108. Wang J, Gou L, Yang H, Shen HW (2018) GANViz: a visual analytics approach to understand the adversarial game. IEEE Trans Vis Comput Graph 24(6):1905–1917CrossRefGoogle Scholar
  109. Watanabe K, Wu HY, Niibe Y, Takahashi S, Fujishiro I (2015) Biclustering multivariate data for correlated subspace mining. IEEE PacificVis:287–294Google Scholar
  110. Wei F, Li W, Liu S (2010) irank: a rank-learn-combine framework for unsupervised ensemble ranking. J Am Soc Inf Sci Technol 61(6):1232–1243Google Scholar
  111. Wen L, Wang J, van der Aalst WM, Huang B, Sun J (2010) Mining process models with prime invisible tasks. Data Knowl Eng 69(10):999–1021CrossRefGoogle Scholar
  112. Wilkinson L (2018) Visualizing big data outliers through distributed aggregation. IEEE Trans Vis Comput Graph 24(1):256–266CrossRefGoogle Scholar
  113. Wongsuphasawat K, Smilkov D, Wexler J, Wilson J, Mané D, Fritz D, Krishnan D, Viégas FB, Wattenberg M (2018) Visualizing dataflow graphs of deep learning models in Tensorflow. IEEE Trans Vis Comput Graph 24(1):1–12CrossRefGoogle Scholar
  114. Wu Y, Wu W, Yang S, Yan Y, Qu H (2015) Interactive visual summary of major communities in a large network. IEEE PacificVis:47–54Google Scholar
  115. Wu Y, Cao N, Gotz D, Tan YP, Keim DA (2016) A survey on visual analytics of social media data. IEEE Trans Multimed 18(11):2135–2148CrossRefGoogle Scholar
  116. Wu HY, Niibe Y, Watanabe K, Takahashi S, Uemura M, Fujishiro I (2017a) Making many-to-many parallel coordinate plots scalable by asymmetric biclustering. In: IEEE PacificVis, pp 305–309Google Scholar
  117. Wu W, Zheng Y, Cao N, Zeng H, Ni B, Qu H, Ni LM (2017b) MobiSeg: interactive region segmentation using heterogeneous mobility data. In: IEEE PacificVis, pp 91–100Google Scholar
  118. Wu Y, Chen Z, Sun G, Xie X, Cao N, Liu S, Cui W (2017c) StreamExplorer: a multi-stage system for visually exploring events in social streams. IEEE Trans Vis Comput Graph 24(10):2758–2772CrossRefGoogle Scholar
  119. Wu H, Jia S, Wang J, Zhang J (2018) M3: visual exploration of spatial relationships between flight trajectories. J Vis 21(3):457–470CrossRefGoogle Scholar
  120. Xia J, Ye F, Chen W, Wang Y, Chen W, Ma Y, Tung AK (2018) LDSScanner: exploratory analysis of low-dimensional structures in high-dimensional datasets. IEEE Trans Vis Comput Graph 24(1):236–245CrossRefGoogle Scholar
  121. Xie C, Zhong W, Mueller K (2017) A visual analytics approach for categorical joint distribution reconstruction from marginal projections. IEEE Trans Vis Comput Graph 23(1):51–60CrossRefGoogle Scholar
  122. Xu P, Cao N, Qu H, Stasko J (2016) Interactive visual co-cluster analysis of bipartite graphs. In: IEEE PacificVis, pp 32–39Google Scholar
  123. Xu P, Mei H, Ren L, Chen W (2017) ViDX: visual diagnostics of assembly line performance in smart factories. IEEE Trans Vis Comput Graph 23(1):291–300CrossRefGoogle Scholar
  124. Xu J, Tao Y, Yan Y, Lin H (2018) VAUT: a visual analytics system of spatiotemporal urban topics in reviews. J Vis 21(3):471–484CrossRefGoogle Scholar
  125. Yan Y, Tao Y, Xu J, Ren S, Lin H (2018) Visual analytics of bike-sharing data based on tensor factorization. J Vis 21(3):495–509CrossRefGoogle Scholar
  126. Yu L, Wu W, Li X, Li G, Ng WS, Ng SK, Huang Z, Arunan A, Watt HM (2015) iVizTRANS: interactive visual learning for home and work place detection from massive public transportation data. IEEE VAST:49–56Google Scholar
  127. Zhang Z, McDonnell KT, Zadok E, Mueller K (2015) Visual correlation analysis of numerical and categorical data on the correlation map. IEEE Trans Vis Comput Graph 21(2):289–303CrossRefGoogle Scholar
  128. Zhang C, Yang J, Zhan FB, Gong X, Brender JD, Langlois PH, Barlowe S, Zhao Y (2016a) A visual analytics approach to high-dimensional logistic regression modeling and its application to an environmental health study. In: IEEE PacificVis, pp 136–143Google Scholar
  129. Zhang Y, Luo W, Mack EA, Maciejewski R (2016b) Visualizing the impact of geographical variations on multivariate clustering. Comput Graph Forum 35(3):101–110CrossRefGoogle Scholar
  130. Zhao J, Sun M, Chen F, Chiu P (2018) BiDots: visual exploration of weighted biclusters. IEEE Trans Vis Comput Graph 24(1):195–204CrossRefGoogle Scholar
  131. Zhou F, Li J, Huang W, Zhao Y, Yuan X, Liang X, Shi Y (2016) Dimension reconstruction for visual exploration of subspace clusters in high-dimensional data. IEEE PacificVis:128–135Google Scholar

Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  1. 1.National Engineering Lab for Big Data Software, and School of SoftwareTsinghua UniversityBeijingChina

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