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SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance

Sacha, Dominik and Kraus, Matthias and Bernard, Jürgen and Behrisch, Michael and Schreck, Tobias and Asano, Yuki and Keim, Daniel A. (2018):
SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.
In: IEEE Transactions on Visualization and Computer Graphics, pp. 120-130, 24, (1), ISSN 1077-2626,
DOI: 10.1109/TVCG.2017.2744805,
[Online-Edition: https://doi.org/10.1109/TVCG.2017.2744805],
[Article]

Abstract

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

Item Type: Article
Erschienen: 2018
Creators: Sacha, Dominik and Kraus, Matthias and Bernard, Jürgen and Behrisch, Michael and Schreck, Tobias and Asano, Yuki and Keim, Daniel A.
Title: SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance
Language: English
Abstract:

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

Journal or Publication Title: IEEE Transactions on Visualization and Computer Graphics
Volume: 24
Number: 1
Uncontrolled Keywords: Visual analytics, Interaction, Visual cluster analysis, Self-organizing maps (SOM), Quality metrics
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 10 Jul 2019 08:26
DOI: 10.1109/TVCG.2017.2744805
Official URL: https://doi.org/10.1109/TVCG.2017.2744805
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