demonstration

Interactive data mining with 3D-parallel-coordinate-trees

Authors:

Hans-Peter Kriegel,

Erich Schubert,

Arthur ZimekAuthors Info & Claims

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

Pages 1009 - 1012

https://doi.org/10.1145/2463676.2463696

Published: 22 June 2013 Publication History

Abstract

Parallel coordinates are an established technique to visualize high-dimensional data, in particular for data mining purposes. A major challenge is the ordering of axes, as any axis can have at most two neighbors when placed in parallel on a 2D plane. By extending this concept to a 3D visualization space we can place several axes next to each other. However, finding a good arrangement often does not necessarily become easier, as still not all axes can be arranged pairwise adjacently to each other. Here, we provide a tool to explore complex data sets using 3D-parallel-coordinate-trees, along with a number of approaches to arrange the axes.

References

[1]

E. Achtert, C. Böhm, J. David, P. Kröger, and A. Zimek. Global correlation clustering based on the Hough transform. Stat. Anal. Data Min., 1(3):111--127, 2008.

Digital Library

[2]

E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, and A. Zimek. Finding hierarchies of subspace clusters. In Proc. PKDD, pages 446--453, 2006.

Digital Library

[3]

E. Achtert, S. Goldhofer, H.-P. Kriegel, E. Schubert, and A. Zimek. Evaluation of clusterings -- metrics and visual support. In Proc. ICDE, pages 1285--1288, 2012.

Digital Library

[4]

E. Achtert, A. Hettab, H.-P. Kriegel, E. Schubert, and A. Zimek. Spatial outlier detection: Data, algorithms, visualizations. In Proc. SSTD, pages 512--516, 2011.

Digital Library

[5]

C. C. Aggarwal, C. M. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In Proc. SIGMOD, pages 61--72, 1999.

Digital Library

[6]

C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional space. In Proc. SIGMOD, pages 70--81, 2000.

Digital Library

[7]

C. C. Aggarwal and P. S. Yu. Outlier detection for high dimensional data. In Proc. SIGMOD, pages 37--46, 2001.

Digital Library

[8]

M. Ankerst, S. Berchtold, and D. A. Keim. Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In Proc. INFOVIS, pages 52--60, 1998.

Digital Library

[9]

C. Baumgartner, K. Kailing, H.-P. Kriegel, P. Kröger, and C. Plant. Subspace selection for clustering high-dimensional data. In Proc. ICDM, pages 11--18, 2004.

Digital Library

[10]

C. Böhm, K. Kailing, H.-P. Kriegel, and P. Kröger. Density connected clustering with local subspace preferences. In Proc. ICDM, pages 27--34, 2004.

Digital Library

[11]

C. Böhm, K. Kailing, P. Kröger, and A. Zimek. Computing clusters of correlation connected objects. In Proc. SIGMOD, pages 455--466, 2004.

Digital Library

[12]

E. Fanea, S. Carpendale, and T. Isenberg. An interactive 3d integration of parallel coordinates and star glyphs. In Proc. INFOVIS, pages 149--156. IEEE, 2005.

Digital Library

[13]

E. W. Forgy. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics, 21:768--769, 1965.

[14]

J. M. Geusebroek, G. J. Burghouts, and A. Smeulders. The Amsterdam Library of Object Images. Int. J. Computer Vision, 61(1):103--112, 2005.

Digital Library

[15]

D. Guo. Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Information Visualization, 2(4):232--246, 2003.

Digital Library

[16]

R. M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE TSAP, 3(6):610--623, 1973.

[17]

P. V. C. Hough. Methods and means for recognizing complex patterns. U.S. Patent 3069654, December 18 1962.

[18]

A. Inselberg. Parallel coordinates: visual multidimensional geometry and its applications. Springer, 2009.

Digital Library

[19]

A. Inselberg and B. Dimsdale. Parallel coordinates: a tool for visualizing multi-dimensional geometry. In Proc. VIS, pages 361--378, 1990.

Digital Library

[20]

J. Johansson, P. Ljung, M. Jern, and M. Cooper. Revealing structure in visualizations of dense 2d and 3d parallel coordinates. Information Visualization, 5(2):125--136, 2006.

Digital Library

[21]

F. Keller, E. Müller, and K. Böhm. HiCS: high contrast subspaces for density-based outlier ranking. In Proc. ICDE, 2012.

Digital Library

[22]

H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek. Outlier detection in axis-parallel subspaces of high dimensional data. In Proc. PAKDD, pages 831--838, 2009.

Digital Library

[23]

H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek. Outlier detection in arbitrarily oriented subspaces. In Proc. ICDM, 2012.

Digital Library

[24]

H.-P. Kriegel, P. Kröger, and A. Zimek. Clustering high dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM TKDD, 3(1):1--58, 2009.

Digital Library

[25]

H.-P. Kriegel, P. Kröger, and A. Zimek. Subspace clustering. WIREs DMKD, 2(4):351--364, 2012.

Digital Library

[26]

S. Ramaswamy, R. Rastogi, and K. Shim. Efficient algorithms for mining outliers from large data sets. In Proc. SIGMOD, pages 427--438, 2000.

Digital Library

[27]

K. Sim, V. Gopalkrishnan, A. Zimek, and G. Cong. A survey on enhanced subspace clustering. Data Min. Knowl. Disc., 2012.

Digital Library

[28]

A. Tatu, G. Albuquerque, M. Eisemann, P. Bak, H. Theisel, M. Magnor, and D. Keim. Automated analytical methods to support visual exploration of high-dimensional data. IEEE TVCG, 17(5):584--597, 2011.

Digital Library

[29]

A. Tatu, F. Maaß, I. Färber, E. Bertini, T. Schreck, T. Seidl, and D. A. Keim. Subspace search and visualization to make sense of alternative clusterings in high-dimensional data. In Proc. VAST, pages 63--72, 2012.

Digital Library

[30]

R. Wegenkittl, H. Löffelmann, and E. Gröller. Visualizing the behaviour of higher dimensional dynamical systems. In Proc. VIS, pages 119--125. IEEE, 1997.

Digital Library

[31]

J. Yang, M. Ward, E. Rundensteiner, and S. Huang. Visual hierarchical dimension reduction for exploration of high dimensional datasets. In Proc. Symp. Data Visualisation 2003, pages 19--28, 2003.

Digital Library

[32]

A. Zimek, E. Schubert, and H.-P. Kriegel. A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min., 5(5):363--387, 2012.

Digital Library

Cited By

Zuo ZLi ZCheng PZhao J(2023)A novel subspace outlier detection method by entropy-based clustering algorithmScientific Reports10.1038/s41598-023-42261-413:1Online publication date: 15-Sep-2023
https://doi.org/10.1038/s41598-023-42261-4
Blumenschein MZhang XPomerenke DKeim DFuchs J(2020)Evaluating Reordering Strategies for Cluster Identification in Parallel CoordinatesComputer Graphics Forum10.1111/cgf.1400039:3(537-549)Online publication date: 18-Jul-2020
https://doi.org/10.1111/cgf.14000
Riahi FSchulte O(2020)Model-based exception mining for object-relational dataData Mining and Knowledge Discovery10.1007/s10618-020-00677-wOnline publication date: 19-Feb-2020
https://doi.org/10.1007/s10618-020-00677-w
Show More Cited By

Index Terms

Interactive data mining with 3D-parallel-coordinate-trees
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

June 2013

1322 pages

ISBN:9781450320375

DOI:10.1145/2463676

General Chairs:
Kenneth Ross
Columbia University
,
Divesh Srivastava
AT&T Research
,
Program Chair:
Dimitris Papadias
HKUST

Copyright © 2013 ACM.

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2013

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Demonstration

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SIGMOD/PODS'13

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SIGMOD

SIGMOD/PODS'13: International Conference on Management of Data

June 22 - 27, 2013

New York, New York, USA

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SIGMOD '13 Paper Acceptance Rate 76 of 372 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Zuo ZLi ZCheng PZhao J(2023)A novel subspace outlier detection method by entropy-based clustering algorithmScientific Reports10.1038/s41598-023-42261-413:1Online publication date: 15-Sep-2023
https://doi.org/10.1038/s41598-023-42261-4
Blumenschein MZhang XPomerenke DKeim DFuchs J(2020)Evaluating Reordering Strategies for Cluster Identification in Parallel CoordinatesComputer Graphics Forum10.1111/cgf.1400039:3(537-549)Online publication date: 18-Jul-2020
https://doi.org/10.1111/cgf.14000
Riahi FSchulte O(2020)Model-based exception mining for object-relational dataData Mining and Knowledge Discovery10.1007/s10618-020-00677-wOnline publication date: 19-Feb-2020
https://doi.org/10.1007/s10618-020-00677-w
Ding KLi JLiu HCulpepper JMoffat ABennett PLerman K(2019)Interactive Anomaly Detection on Attributed NetworksProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3290964(357-365)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3290964
Cui W(2019)Visual Analytics: A Comprehensive OverviewIEEE Access10.1109/ACCESS.2019.29237367(81555-81573)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2923736
Sand CLechler TSchuh PFranke J(2018)Potentials for Error Detection and Process Visualization in Assembly Lines Using a Parallel Coordinates PlotApplied Mechanics and Materials10.4028/www.scientific.net/AMM.882.10882(10-16)Online publication date: Jul-2018
https://doi.org/10.4028/www.scientific.net/AMM.882.10
Schubert EGertz MSacharidis DGamper JBöhlen M(2018)Numerically stable parallel computation of (co-)varianceProceedings of the 30th International Conference on Scientific and Statistical Database Management10.1145/3221269.3223036(1-12)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.1145/3221269.3223036
Huang XSong QLi JHu XChang YZhai CLiu YMaarek Y(2018)Exploring Expert Cognition for Attributed Network EmbeddingProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159655(270-278)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.1145/3159652.3159655
Bandaragoda TTing KAlbrecht DLiu FZhu YWells J(2018)Isolation‐based anomaly detection using nearest‐neighbor ensemblesComputational Intelligence10.1111/coin.1215634:4(968-998)Online publication date: 5-Jan-2018
https://doi.org/10.1111/coin.12156
Okada Y(2018)Time-Tunnel: 3D Visualization Tool and Its Aspects as 3D Parallel Coordinates2018 22nd International Conference Information Visualisation (IV)10.1109/iV.2018.00019(50-55)Online publication date: Jul-2018
https://doi.org/10.1109/iV.2018.00019
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