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Assisted Descriptor Selection Based on Visual Comparative Data Analysis

Bremm, Sebastian and Landesberger, Tatiana von and Bernard, Jürgen and Schreck, Tobias (2011):
Assisted Descriptor Selection Based on Visual Comparative Data Analysis.
In: Computer Graphics Forum, pp. 891-900, 30, (3), DOI: 10.1111/j.1467-8659.2011.01938.x, [Article]

Abstract

Exploration and selection of data descriptors representing objects using a set of features are important components in many data analysis tasks. Usually, for a given dataset, an optimal data description does not exist, as the suitable data representation is strongly use case dependent. Many solutions for selecting a suitable data description have been proposed. In most instances, they require data labels and often are black box approaches. Non-expert users have difficulties to comprehend the coherency of input, parameters, and output of these algorithms. Alternative approaches, interactive systems for visual feature selection, overburden the user with an overwhelming set of options and data views. Therefore, it is essential to offer the users guidance in this analytical process. In this paper, we present a novel system for data description selection, which facilitates the user's access to the data analysis process. As finding of suitable data description consists of several steps, we support the user with guidance. Our system combines automatic data analysis with interactive visualizations. By this, the system provides a recommendation for suitable data descriptor selections. It supports the comparison of data descriptors with differing dimensionality for unlabeled data. We propose specialized scores and interactive views for descriptor comparison. The visualization techniques are scatterplot-based and grid-based. For the latter case, we apply Self-Organizing Maps as adaptive grids which are well suited for large multi-dimensional data sets. As an example, we demonstrate the usability of our system on a real-world biochemical application.

Item Type: Article
Erschienen: 2011
Creators: Bremm, Sebastian and Landesberger, Tatiana von and Bernard, Jürgen and Schreck, Tobias
Title: Assisted Descriptor Selection Based on Visual Comparative Data Analysis
Language: English
Abstract:

Exploration and selection of data descriptors representing objects using a set of features are important components in many data analysis tasks. Usually, for a given dataset, an optimal data description does not exist, as the suitable data representation is strongly use case dependent. Many solutions for selecting a suitable data description have been proposed. In most instances, they require data labels and often are black box approaches. Non-expert users have difficulties to comprehend the coherency of input, parameters, and output of these algorithms. Alternative approaches, interactive systems for visual feature selection, overburden the user with an overwhelming set of options and data views. Therefore, it is essential to offer the users guidance in this analytical process. In this paper, we present a novel system for data description selection, which facilitates the user's access to the data analysis process. As finding of suitable data description consists of several steps, we support the user with guidance. Our system combines automatic data analysis with interactive visualizations. By this, the system provides a recommendation for suitable data descriptor selections. It supports the comparison of data descriptors with differing dimensionality for unlabeled data. We propose specialized scores and interactive views for descriptor comparison. The visualization techniques are scatterplot-based and grid-based. For the latter case, we apply Self-Organizing Maps as adaptive grids which are well suited for large multi-dimensional data sets. As an example, we demonstrate the usability of our system on a real-world biochemical application.

Journal or Publication Title: Computer Graphics Forum
Volume: 30
Number: 3
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Business Field: Visual decision support, Research Area: Generalized digital documents, Interactive visualization, Feature selection, Self-organizing maps (SOM)
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1111/j.1467-8659.2011.01938.x
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