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Heidi matrix: nearest neighbor driven high dimensional data visualization

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Kamalakar KarlapalemAuthors Info & Claims

VAKD '09: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration

Pages 83 - 92

https://doi.org/10.1145/1562849.1562859

Published: 28 June 2009 Publication History

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Abstract

Identifying patterns in large high dimensional data sets is a challenge. As the number of dimensions increases, the patterns in the data sets tend to be more prominent in the subspaces than the original dimensional space. A system to facilitate presentation of such subspace oriented patterns in high dimensional data sets is required to understand the data.

Heidi is a high dimensional data visualization system that captures and visualizes the closeness of points across various subspaces of the dimensions; thus, helping to understand the data. The core concept behind Heidi is based on prominence of patterns within the nearest neighbor relations between pairs of points across the subspaces.

Given a d-dimensional data set as input, Heidi system generates a 2-D matrix represented as a color image. This representation gives insight into (i) how the clusters are placed with respect to each other, (ii) characteristics of placement of points within a cluster in all the subspaces and (iii) characteristics of overlapping clusters in various subspaces.

A sample of results displayed and discussed in this paper illustrate how Heidi Visualization can be interpreted.

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Newen CMuller E(2022)Unsupervised DeepView: Global Explainability of Uncertainties for High Dimensional Data2022 IEEE International Conference on Knowledge Graph (ICKG)10.1109/ICKG55886.2022.00032(196-202)Online publication date: Nov-2022
https://doi.org/10.1109/ICKG55886.2022.00032
Newen CMuller E(2022)Unsupervised DeepView: Global Uncertainty Visualization for High Dimensional Data2022 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW58026.2022.00086(1-8)Online publication date: Nov-2022
https://doi.org/10.1109/ICDMW58026.2022.00086
Djuric NVucetic S(2013)Efficient Visualization of Large-Scale Data Tables through Reordering and Entropy Minimization2013 IEEE 13th International Conference on Data Mining10.1109/ICDM.2013.63(121-130)Online publication date: Dec-2013
https://doi.org/10.1109/ICDM.2013.63
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Index Terms

Heidi matrix: nearest neighbor driven high dimensional data visualization
1. Computing methodologies
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Graphics input devices

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Published In

VAKD '09: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration

June 2009

92 pages

ISBN:9781605586700

DOI:10.1145/1562849

General Chairs:
Kai Puolamäki
Helsinki Institute for Information Technology HIIT
,
Heikki Mannila
Helsinki Institute for Information Technology HIIT
,
Alessio Bertone
Danube University Krems
,
Silvia Miksch
Danube University Krems

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2009

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 28, 2009

Paris, France

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Cited By

View all

Newen CMuller E(2022)Unsupervised DeepView: Global Explainability of Uncertainties for High Dimensional Data2022 IEEE International Conference on Knowledge Graph (ICKG)10.1109/ICKG55886.2022.00032(196-202)Online publication date: Nov-2022
https://doi.org/10.1109/ICKG55886.2022.00032
Newen CMuller E(2022)Unsupervised DeepView: Global Uncertainty Visualization for High Dimensional Data2022 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW58026.2022.00086(1-8)Online publication date: Nov-2022
https://doi.org/10.1109/ICDMW58026.2022.00086
Djuric NVucetic S(2013)Efficient Visualization of Large-Scale Data Tables through Reordering and Entropy Minimization2013 IEEE 13th International Conference on Data Mining10.1109/ICDM.2013.63(121-130)Online publication date: Dec-2013
https://doi.org/10.1109/ICDM.2013.63
Farber ITatu AKeim DSeidl TMaas FSchreck TBertini E(2012)Subspace search and visualization to make sense of alternative clusterings in high-dimensional dataProceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)10.1109/VAST.2012.6400488(63-72)Online publication date: 14-Oct-2012
https://dl.acm.org/doi/10.1109/VAST.2012.6400488
Agrawal SVadapalli SKarlapalem K(2011)Heidi visualization of R-tree structures over high dimensional dataProceedings of the 23rd international conference on Scientific and statistical database management10.5555/2032397.2032447(565-567)Online publication date: 20-Jul-2011
https://dl.acm.org/doi/10.5555/2032397.2032447
Agrawal SVadapalli SKarlapalem K(2011)Heidi Visualization of R-tree Structures over High Dimensional DataScientific and Statistical Database Management10.1007/978-3-642-22351-8_39(565-567)Online publication date: 2011
https://doi.org/10.1007/978-3-642-22351-8_39
Zhuhadar LNasraoui OWyatt RYang R(2010)Visual knowledge representation of conceptual semantic networksSocial Network Analysis and Mining10.1007/s13278-010-0008-21:3(219-229)Online publication date: 25-Nov-2010
https://doi.org/10.1007/s13278-010-0008-2
Vadapalli SKarlapalem K(2009)BEADS: High dimensional data cluster visualization2009 IEEE Symposium on Visual Analytics Science and Technology10.1109/VAST.2009.5333417(235-236)Online publication date: Oct-2009
https://doi.org/10.1109/VAST.2009.5333417

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