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We have provided a survey of each of these considerations, describing existing research and discussing relevant design decisions applicable to current and future systems

Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics.

IEEE Transactions on Visualization and Computer Graphics, no. 1 (2018): 131-141

Cited by: 60|Views17
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Abstract

Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms fr...More

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Introduction
  • Visual metaphors for exploring high-dimensional datasets come in a variety of forms, each with their own strengths and weaknesses in both visualization and interaction [37, 69].
  • One frequently used method of visual abstraction is to reduce a high-dimensional dataset into a low-dimensional space while preserving properties of the high-dimensional structure.
  • Such dimension reduction algorithms are useful abstractions because some of the dimensions in the dataset may not be essential to understanding the underlying patterns in the dataset [38].
  • The visualization tasks associated with dimension reduction algorithms have been well studied [14, 15]
Highlights
  • Visual metaphors for exploring high-dimensional datasets come in a variety of forms, each with their own strengths and weaknesses in both visualization and interaction [37, 69]
  • The visualization tasks associated with dimension reduction algorithms have been well studied [14, 15]
  • Combining dimension reduction and clustering algorithms into the same visualization system provides a number of opportunities for visualization and interaction design
  • Including a machine learning aspect into a visualization system to permit the dimension reduction and clustering algorithms to learn from the actions of the analyst presents a number of additional challenges for interaction design
  • We have provided a survey of each of these considerations, describing existing research and discussing relevant design decisions applicable to current and future systems
  • As the dimension reduction and clustering algorithms are competing in the same visualization, what features should be emphasized in the visualization to best address the problem?
Methods
  • Design Decision

    In general, clustering places an emphasis on relationships within and between clusters.
  • Dimension reduction emphasizes observationto-observation relationships.
  • Which of these tasks is the primary goal of the analyst?.
  • Which order and interaction of dimension reduction and clustering algorithms best models the task that the visualization system is addressing?.
  • The students initially formed the clusters of Edible and Inedible animals and positioned those clusters in space, initially mimicking the cluster preprocessing pipeline
  • The transition from this projection into a spectrum of Edibility amounts to iterative and interactive refinement of those initial clusters into a broader projection.
  • The pipelines could not successfully model this student behavior
Conclusion
  • Combining dimension reduction and clustering algorithms into the same visualization system provides a number of opportunities for visualization and interaction design.
  • A notion of iterative refinement, in which the analyst gradually trains the algorithms and offers corrections to mistakes at each iteration is necessary in these cases
  • Such an iterative refinement process mimics Pirolli and Card’s Sensemaking Process [76].The combination of dimension reduction and clustering algorithms represents an immense design space, including considerations of algorithm selection and order, tasks, visualization, and interaction.
  • The authors have provided a survey of each of these considerations, describing existing research and discussing relevant design decisions applicable to current and future systems.
Summary
  • Introduction:

    Visual metaphors for exploring high-dimensional datasets come in a variety of forms, each with their own strengths and weaknesses in both visualization and interaction [37, 69].
  • One frequently used method of visual abstraction is to reduce a high-dimensional dataset into a low-dimensional space while preserving properties of the high-dimensional structure.
  • Such dimension reduction algorithms are useful abstractions because some of the dimensions in the dataset may not be essential to understanding the underlying patterns in the dataset [38].
  • The visualization tasks associated with dimension reduction algorithms have been well studied [14, 15]
  • Methods:

    Design Decision

    In general, clustering places an emphasis on relationships within and between clusters.
  • Dimension reduction emphasizes observationto-observation relationships.
  • Which of these tasks is the primary goal of the analyst?.
  • Which order and interaction of dimension reduction and clustering algorithms best models the task that the visualization system is addressing?.
  • The students initially formed the clusters of Edible and Inedible animals and positioned those clusters in space, initially mimicking the cluster preprocessing pipeline
  • The transition from this projection into a spectrum of Edibility amounts to iterative and interactive refinement of those initial clusters into a broader projection.
  • The pipelines could not successfully model this student behavior
  • Conclusion:

    Combining dimension reduction and clustering algorithms into the same visualization system provides a number of opportunities for visualization and interaction design.
  • A notion of iterative refinement, in which the analyst gradually trains the algorithms and offers corrections to mistakes at each iteration is necessary in these cases
  • Such an iterative refinement process mimics Pirolli and Card’s Sensemaking Process [76].The combination of dimension reduction and clustering algorithms represents an immense design space, including considerations of algorithm selection and order, tasks, visualization, and interaction.
  • The authors have provided a survey of each of these considerations, describing existing research and discussing relevant design decisions applicable to current and future systems.
Tables
  • Table1: A selection of dimension reduction algorithms, organized by the complexity of manifold each can learn: linear manifolds, nonlinear manifolds, and algorithms that have implementations of both types
  • Table2: Sample exploratory data analysis tasks, organized by stage in the data analysis process (rows) and algorithm family (columns)
  • Table3: Sample interactions, organized by type of interaction (rows) and by the type of algorithm affected by the interaction (columns)
  • Table4: A summary of the design challenges and questions discussed throughout the paper regarding the combination of dimension reduction and clustering algorithms
Download tables as Excel
Funding
  • This research was supported by NSF Grants IIS-1447416, IIS-1633363, and DGE-1545362, as well as by a grant from General Dynamics Mission Systems
Reference
  • J. Abello, F. V. Ham, and N. Krishnan. Ask-graphview: A large scale graph visualization system. IEEE Transactions on Visualization and Computer Graphics, 12(5):669–676, Sept 2006. doi: 10.1109/TVCG.2006.120
    Locate open access versionFindings
  • C. C. Aggarwal, A. Hinneburg, and D. A. Keim. On the Surprising Behavior of Distance Metrics in High Dimensional Space, pp. 420–434. Springer Berlin Heidelberg, Berlin, Heidelberg, 2001. doi: 10.1007/3-540 -44503-X 27
    Findings
  • M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty. A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Transactions on Medical Imaging, 21(3):193–199, March 2002. doi: 10.1109/42.996338
    Locate open access versionFindings
  • M. S. Aldenderfer and R. K. Blashfield. Cluster analysis. SAGE publications, Beverly Hills, USA, 1984.
    Google ScholarFindings
  • B. Alper, N. Riche, G. Ramos, and M. Czerwinski. Design study of linesets, a novel set visualization technique. IEEE Transactions on Visualization and Computer Graphics, 17(12):2259–2267, Dec 2011. doi: 10.1109/ TVCG.2011.186
    Google ScholarLocate open access versionFindings
  • J. Alsakran, Y. Chen, Y. Zhao, J. Yang, and D. Luo. Streamit: Dynamic visualization and interactive exploration of text streams. In 2011 IEEE Pacific Visualization Symposium, pp. 131–138, March 2011. doi: 10.1109/ PACIFICVIS.2011.5742382
    Google ScholarLocate open access versionFindings
  • R. Amar, J. Eagan, and J. Stasko. Low-level components of analytic activity in information visualization. In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., pp. 111–117, Oct 2005. doi: 10. 1109/INFVIS.2005.1532136
    Locate open access versionFindings
  • C. Andrews, A. Endert, and C. North. Space to think: Large highresolution displays for sensemaking. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, pp. 55–64. ACM, New York, NY, USA, 2010. doi: 10.1145/1753326.1753336
    Locate open access versionFindings
  • J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell, MA, USA, 1981.
    Google ScholarFindings
  • D. M. Blei and M. I. Jordan. Variational inference for dirichlet process mixtures. Bayesian Analysis, 1:121–144, 2005.
    Google ScholarLocate open access versionFindings
  • D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022, 2003.
    Google ScholarLocate open access versionFindings
  • L. Bradel, C. North, L. House, and S. Leman. Multi-model semantic interaction for text analytics. In 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 163–172, Oct 2014. doi: 10.1109/ VAST.2014.7042492
    Google ScholarLocate open access versionFindings
  • P. S. Bradley, U. Fayyad, and C. Reina. Scaling clustering algorithms to large databases. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, KDD’98, pp. 9–15. AAAI Press, 1998.
    Google ScholarLocate open access versionFindings
  • M. Brehmer and T. Munzner. A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12):2376–2385, Dec 2013. doi: 10.1109/TVCG.2013.124
    Locate open access versionFindings
  • M. Brehmer, M. Sedlmair, S. Ingram, and T. Munzner. Visualizing dimensionally-reduced data: Interviews with analysts and a characterization of task sequences. In Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization, BELIV ’14, pp. 1–8. ACM, New York, NY, USA, 2014. doi: 10.1145/2669557. 2669559
    Locate open access versionFindings
  • E. T. Brown, J. Liu, C. E. Brodley, and R. Chang. Dis-function: Learning distance functions interactively. In 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 83–92, Oct 2012. doi: 10. 1109/VAST.2012.6400486
    Locate open access versionFindings
  • A. Buja, D. Cook, and D. F. Swayne. Interactive high-dimensional data visualization. Journal of Computational and Graphical Statistics, 5(1):78– 99, 1996. doi: 10.1080/10618600.1996.10474696
    Locate open access versionFindings
  • J. D. Carroll and J.-J. Chang. Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika, 35(3):283–319, 1970.
    Google ScholarLocate open access versionFindings
  • H. Chen, W. Chen, H. Mei, Z. Liu, K. Zhou, W. Chen, W. Gu, and K. L. Ma. Visual abstraction and exploration of multi-class scatterplots. IEEE Transactions on Visualization and Computer Graphics, 20(12):1683–1692, Dec 2014. doi: 10.1109/TVCG.2014.2346594
    Locate open access versionFindings
  • X. Chen, J. Z. Self, L. House, and C. North. Be the data: A new approach for immersive analytics. In IEEE Virtual Reality 2016 Workshop on Immersive Analytics, 03/2016.
    Google ScholarLocate open access versionFindings
  • J. Choo, S. Bohn, and H. Park. Two-stage framework for visualization of clustered high dimensional data. In 2009 IEEE Symposium on Visual Analytics Science and Technology, pp. 67–74, Oct 2009. doi: 10.1109/ VAST.2009.5332629
    Google ScholarLocate open access versionFindings
  • J. Choo, C. Lee, C. K. Reddy, and H. Park. Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Transactions on Visualization and Computer Graphics, 19(12):1992–2001, Dec 2013. doi: 10.1109/TVCG.2013.212
    Locate open access versionFindings
  • J. Chuang and D. J. Hsu. Human-centered interactive clustering for data analysis. Conference on Neural Information Processing Systems (NIPS). Workshop on Human-Propelled Machine Learning, 2014.
    Google ScholarLocate open access versionFindings
  • C. Collins, G. Penn, and S. Carpendale. Bubble sets: Revealing set relations with isocontours over existing visualizations. IEEE Transactions on Visualization and Computer Graphics, 15(6):1009–1016, Nov 2009. doi: 10.1109/TVCG.2009.122
    Locate open access versionFindings
  • R. Cordeiro de Amorim and P. Komisarczuk. On Initializations for the Minkowski Weighted K-Means, pp. 45–55. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012. doi: 10.1007/978-3-642-34156-4 6
    Findings
  • I. S. Dhillon and D. S. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 42(1):143–175, 2001. doi: 10.1023/A:1007612920971
    Locate open access versionFindings
  • C. Ding and X. He. K-means clustering via principal component analysis. In Proceedings of the Twenty-first International Conference on Machine Learning, ICML ’04, pp. 29–. ACM, New York, NY, USA, 2004. doi: 10. 1145/1015330.1015408
    Locate open access versionFindings
  • C. Ding and T. Li. Adaptive dimension reduction using discriminant analysis and k-means clustering. In Proceedings of the 24th International Conference on Machine Learning, ICML ’07, pp. 521–5ACM, New York, NY, USA, 2007. doi: 10.1145/1273496.1273562
    Locate open access versionFindings
  • D. L. Donoho. High-dimensional data analysis: The curses and blessings of dimensionality. In AMS Conference on Math Challenges of the 21st Century, 2000.
    Google ScholarLocate open access versionFindings
  • J. C. Dunn. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3):32–57, 1973. doi: 10.1080/01969727308546046
    Locate open access versionFindings
  • T. Dwyer, Y. Koren, and K. Marriott. Ipsep-cola: An incremental procedure for separation constraint layout of graphs. IEEE Transactions on Visualization and Computer Graphics, 12(5):821–828, Sept 2006. doi: 10. 1109/TVCG.2006.156
    Locate open access versionFindings
  • C. F. Eick, N. Zeidat, and Z. Zhao. Supervised clustering - algorithms and benefits. In 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 774–776, Nov 2004. doi: 10.1109/ICTAI.2004.111
    Locate open access versionFindings
  • A. Endert, S. Fox, D. Maiti, S. Leman, and C. North. The semantics of clustering: Analysis of user-generated spatializations of text documents. In Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI ’12, pp. 555–562. ACM, New York, NY, USA, 2012. doi: 10.1145/2254556.2254660
    Locate open access versionFindings
  • A. Endert, C. Han, D. Maiti, L. House, S. Leman, and C. North. Observation-level interaction with statistical models for visual analytics. In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 121–130, Oct 2011. doi: 10.1109/VAST.2011.6102449
    Locate open access versionFindings
  • A. Endert, M. S. Hossain, N. Ramakrishnan, C. North, P. Fiaux, and C. Andrews. The human is the loop: new directions for visual analytics. Journal of Intelligent Information Systems, 43(3):411–435, 2014. doi: 10. 1007/s10844-014-0304-9
    Locate open access versionFindings
  • V. Estivill-Castro. Why so many clustering algorithms: A position paper. SIGKDD Explor. Newsl., 4(1):65–75, June 2002. doi: 10.1145/568574. 568575
    Locate open access versionFindings
  • U. M. Fayyad, A. Wierse, and G. G. Grinstein. Information visualization in data mining and knowledge discovery. Morgan Kaufmann, 2002.
    Google ScholarFindings
  • I. K. Fodor. A Survey of Dimension Reduction Techniques. May 2002. doi: 10.2172/15002155
    Findings
  • S. L. France and J. D. Carroll. Two-way multidimensional scaling: A review. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(5):644–661, Sept 2011. doi: 10.1109/ TSMCC.2010.2078502
    Google ScholarLocate open access versionFindings
  • J. H. Friedman and J. W. Tukey. A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers, C-23(9):881– 890, Sept 1974. doi: 10.1109/T-C.1974.224051
    Locate open access versionFindings
  • E. R. Gansner, Y. Hu, and S. Kobourov. Gmap: Visualizing graphs and clusters as maps. In 2010 IEEE Pacific Visualization Symposium (PacificVis), pp. 201–208, March 2010. doi: 10.1109/PACIFICVIS.2010. 5429590
    Locate open access versionFindings
  • I. Guyon and A. Elisseeff. An introduction to variable and feature selection. J. Mach. Learn. Res., 3:1157–1182, Mar. 2003.
    Google ScholarLocate open access versionFindings
  • H. H. Harman. Modern factor analysis. 1960.
    Google ScholarFindings
  • J. Heer and D. Boyd. Vizster: visualizing online social networks. In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., pp. 32–39, Oct 2005. doi: 10.1109/INFVIS.2005.1532126
    Locate open access versionFindings
  • C. Heine and G. Scheuermann. Manual clustering refinement using interaction with blobs. In Proceedings of the 9th Joint Eurographics / IEEE VGTC Conference on Visualization, EUROVIS’07, pp. 59–66. Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 2007. doi: 10. 2312/VisSym/EuroVis07/059-066
    Locate open access versionFindings
  • L. House, S. Leman, and C. Han. Bayesian visual analytics: Bava. Statistical Analysis and Data Mining, 8(1):1–13, 2015. doi: 10.1002/sam. 11253
    Locate open access versionFindings
  • X. Hu, L. Bradel, D. Maiti, L. House, C. North, and S. Leman. Semantics of directly manipulating spatializations. IEEE Transactions on Visualization and Computer Graphics, 19(12):2052–2059, Dec 2013. doi: 10. 1109/TVCG.2013.188
    Locate open access versionFindings
  • J. Z. Huang, M. K. Ng, H. Rong, and Z. Li. Automated variable weighting in k-means type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5):657–668, May 2005. doi: 10.1109/TPAMI. 2005.95
    Locate open access versionFindings
  • A. Hyvarinen, J. Karhunen, and E. Oja. Independent component analysis, vol.
    Google ScholarLocate open access versionFindings
  • 46. John Wiley & Sons, 2004.
    Google ScholarFindings
  • [50] S. Ingram, T. Munzner, and M. Olano. Glimmer: Multilevel mds on the gpu. IEEE Transactions on Visualization and Computer Graphics, 15(2):249–261, March 2009. doi: 10.1109/TVCG.2008.85
    Locate open access versionFindings
  • [51] R. Jianu, A. Rusu, Y. Hu, and D. Taggart. How to display group information on node-link diagrams: An evaluation. IEEE Transactions on Visualization and Computer Graphics, 20(11):1530–1541, Nov 2014. doi: 10.1109/TVCG.2014.2315995
    Locate open access versionFindings
  • [52] P. Joia, F. Petronetto, and L. G. Nonato. Uncovering representative groups in multidimensional projections. In Proceedings of the 2015 Eurographics Conference on Visualization, EuroVis ’15, pp. 281–290. Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 2015. doi: 10.1111/ cgf.12640
    Locate open access versionFindings
  • [53] E. Kandogan. Star coordinate: A multi-dimensional visualization technique with uniform treatment of dimensions. In Proceedings of the IEEE Information Visualization Symposium, vol. 650, p. 22.
    Google ScholarLocate open access versionFindings
  • [54] E. Kandogan. Just-in-time annotation of clusters, outliers, and trends in point-based data visualizations. In 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 73–82, Oct 2012. doi: 10. 1109/VAST.2012.6400487
    Locate open access versionFindings
  • [55] T. Kohonen. The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480, Sep 1990. doi: 10.1109/5.58325
    Locate open access versionFindings
  • [56] H.-P. Kriegel, P. Kroger, and A. Zimek. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans. Knowl. Discov. Data, 3(1):1:1–1:58, Mar. 2009. doi: 10.1145/1497577.1497578
    Locate open access versionFindings
  • [57] H. Lee, J. Kihm, J. Choo, J. Stasko, and H. Park. ivisclustering: An interactive visual document clustering via topic modeling. Computer Graphics Forum, 31(3pt3):1155–1164, 2012. doi: 10.1111/j.1467-8659. 2012.03108.x
    Locate open access versionFindings
  • [58] J. A. Lee and M. Verleysen. Nonlinear dimensionality reduction. Springer Science & Business Media, 2007.
    Google ScholarFindings
  • [59] S. C. Leman, L. House, D. Maiti, A. Endert, and C. North. Visual to parametric interaction (v2pi). PloS one, 8(3), 2013.
    Google ScholarLocate open access versionFindings
  • [60] M. Levandowsky and D. Winter. Distance between sets. Nature, 234(5323):34–35, 1971.
    Google ScholarLocate open access versionFindings
  • [61] S. Liu, D. Maljovec, B. Wang, P. T. Bremer, and V. Pascucci. Visualizing high-dimensional data: Advances in the past decade. IEEE Transactions on Visualization and Computer Graphics, 23(3):1249–1268, March 2017. doi: 10.1109/TVCG.2016.2640960
    Locate open access versionFindings
  • [62] S. Liu, B. Wang, P.-T. Bremer, and V. Pascucci. Distortion-guided structure-driven interactive exploration of high-dimensional data. Computer Graphics Forum, 33(3):101–110, 2014. doi: 10.1111/cgf.12366
    Locate open access versionFindings
  • [63] S. Liu, M. X. Zhou, S. Pan, W. Qian, W. Cai, and X. Lian. Interactive, topic-based visual text summarization and analysis. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09, pp. 543–552. ACM, New York, NY, USA, 2009. doi: 10. 1145/1645953.1646023
    Locate open access versionFindings
  • [64] S. Lloyd. Least squares quantization in pcm. IEEE Transactions on Information Theory, 28(2):129–137, Mar 1982. doi: 10.1109/TIT.1982. 1056489
    Locate open access versionFindings
  • [65] L. v. d. Maaten and G. Hinton. Visualizing data using t-sne. J. Mach. Learn. Res., 9:2579–2605, Sept. 2008.
    Google ScholarLocate open access versionFindings
  • [66] G. M. H. Mamani, F. M. Fatore, L. G. Nonato, and F. V. Paulovich. User-driven feature space transformation. Computer Graphics Forum, 32(3pt3):291–299, 2013. doi: 10.1111/cgf.12116
    Locate open access versionFindings
  • [67] A. Mayorga and M. Gleicher. Splatterplots: Overcoming overdraw in scatter plots. IEEE Transactions on Visualization and Computer Graphics, 19(9):1526–1538, Sept 2013. doi: 10.1109/TVCG.2013.65
    Locate open access versionFindings
  • [68] K. Misue, P. Eades, W. Lai, and K. Sugiyama. Layout adjustment and the mental map. Journal of Visual Languages & Computing, 6(2):183 – 210, 1995. doi: 10.1006/jvlc.1995.1010
    Locate open access versionFindings
  • [69] T. Munzner. Visualization Analysis and Design. CRC Press, 2014.
    Google ScholarFindings
  • [70] E. J. Nam, Y. Han, K. Mueller, A. Zelenyuk, and D. Imre. Clustersculptor: A visual analytics tool for high-dimensional data. In 2007 IEEE Symposium on Visual Analytics Science and Technology, pp. 75–82, Oct 2007. doi: 10.1109/VAST.2007.4388999
    Locate open access versionFindings
  • [71] A. Y. Ng, M. I. Jordan, Y. Weiss, et al. On spectral clustering: Analysis and an algorithm. In NIPS, vol. 14, pp. 849–856, 2001.
    Google ScholarLocate open access versionFindings
  • [72] D. Niu, J. G. Dy, and M. I. Jordan. Dimensionality reduction for spectral clustering. In Proceedings of the 14th International Conference Artificial Intelligence and Statistics, AISTATS ’11, pp. 552–560. ACM, New York, NY, USA, 2011.
    Google ScholarLocate open access versionFindings
  • [73] F. Paulovich, D. Eler, J. Poco, C. Botha, R. Minghim, and L. Nonato. Piecewise laplacian-based projection for interactive data exploration and organization. Computer Graphics Forum, 30(3):1091–1100, 2011. doi: 10.1111/j.1467-8659.2011.01958.x
    Locate open access versionFindings
  • [74] K. Pearson. Principal components analysis. The London, Edinburgh and Dublin Philosophical Magazine and Journal, 6(2):566, 1901.
    Google ScholarLocate open access versionFindings
  • [75] D. Pelleg, A. W. Moore, et al. X-means: Extending k-means with efficient estimation of the number of clusters. In ICML, vol. 1, pp. 727–734, 2000.
    Google ScholarLocate open access versionFindings
  • [76] P. Pirolli and S. Card. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. Proceedings of International Conference on Intelligence Analysis, pp. 2–4, 2005.
    Google ScholarLocate open access versionFindings
  • [77] B. Rieck and H. Leitte. Persistent homology for the evaluation of dimensionality reduction schemes. Computer Graphics Forum, 34(3):431–440, 2015. doi: 10.1111/cgf.12655
    Locate open access versionFindings
  • [78] D. Ro and H. Pe. Pattern classification and scene analysis. John Wiley & Sons, New York, USA, 1973.
    Google ScholarFindings
  • [79] B. Saket, P. Simonetto, S. Kobourov, and K. Brner. Node, node-link, and node-link-group diagrams: An evaluation. IEEE Transactions on Visualization and Computer Graphics, 20(12):2231–2240, Dec 2014. doi: 10.1109/TVCG.2014.2346422
    Locate open access versionFindings
  • [80] J. Z. Self, R. K. Vinayagam, J. T. Fry, and C. North. Bridging the gap between user intention and model parameters for human-in-the-loop data analytics. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA ’16, pp. 3:1–3:6. ACM, New York, NY, USA, 2016. doi: 10.1145/2939502.2939505
    Locate open access versionFindings
  • [81] P.-N. Tan, M. Steinbach, and V. Kumar. Data mining cluster analysis: basic concepts and algorithms. In Introduction to data mining, chap.
    Google ScholarFindings
  • 8. Pearson Education India, 2013.
    Google ScholarFindings
  • [82] J. B. Tenenbaum, V. d. Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319– 2323, 2000. doi: 10.1126/science.290.5500.2319
    Locate open access versionFindings
  • [83] R. L. Thorndike. Who belongs in the family? Psychometrika, 18(4):267– 276, 1953. doi: 10.1007/BF02289263
    Locate open access versionFindings
  • [84] M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3):611–622, 1999.
    Google ScholarLocate open access versionFindings
  • [85] W. S. Torgerson. Theory and methods of scaling. 1958.
    Google ScholarFindings
  • [86] C. Turkay, P. Filzmoser, and H. Hauser. Brushing dimensions - a dual visual analysis model for high-dimensional data. IEEE Transactions on Visualization and Computer Graphics, 17(12):2591–2599, Dec 2011. doi: 10.1109/TVCG.2011.178
    Locate open access versionFindings
  • [87] F. Valafar. Pattern recognition techniques in microarray data analysis. Annals of the New York Academy of Sciences, 980(1):41–64, 2002. doi: 10.1111/j.1749-6632.2002.tb04888.x
    Locate open access versionFindings
  • [88] T. von Landesberger, S. Fiebig, S. Bremm, A. Kuijper, and D. W. Fellner. Interaction Taxonomy for Tracking of User Actions in Visual Analytics Applications, pp. 653–670. Springer New York, New York, NY, 2014. doi: 10.1007/978-1-4614-7485-2 26
    Findings
  • [89] K. Wagstaff, C. Cardie, S. Rogers, S. Schrodl, et al. Constrained k-means clustering with background knowledge. In Proceedings of the Eighteenth International Conference on Machine Learning, vol. 1, pp. 577–584, 2001.
    Google ScholarLocate open access versionFindings
  • [90] J. Wenskovitch and C. North. Observation-level interaction with clustering and dimension reduction algorithms. In Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, HILDA’17, pp. 14:1–14:6. ACM, New York, NY, USA, 2017. doi: 10.1145/3077257.3077259
    Locate open access versionFindings
  • [91] A. Wismuller, M. Verleysen, M. Aupetit, and J. A. Lee. Recent advances in nonlinear dimensionality reduction, manifold and topological learning. In ESANN, 2010.
    Google ScholarLocate open access versionFindings
  • [92] R. Xu and D. Wunsch. Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3):645–678, May 2005. doi: 10.1109/TNN.2005. 845141
    Locate open access versionFindings
  • [93] J. S. Yi, Y. a. Kang, and J. Stasko. Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics, 13(6):1224–1231, Nov 2007. doi: 10.1109/TVCG.2007.70515
    Locate open access versionFindings
  • [94] H. Zha, X. He, C. Ding, M. Gu, and H. D. Simon. Spectral relaxation for k-means clustering. In Advances in Neural Information Processing Systems, pp. 1057–1064, 2002.
    Google ScholarLocate open access versionFindings
Author
John Wenskovitch
John Wenskovitch
Ian Crandell
Ian Crandell
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