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  4. Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data
 
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2021
Conference Paper
Titel

Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data

Abstract
The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.
Author(s)
Chakrabarti, A.
Das, A.
Cochez, M.
Quix, C.
Hauptwerk
Advances in Databases and Information Systems. 25th European Conference, ADBIS 2021. Proceedings
Konferenz
European Conference on Advances in Databases and Information Systems (ADBIS) 2021
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DOI
10.1007/978-3-030-82472-3_14
Language
English
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Fraunhofer-Institut für Angewandte Informationstechnik FIT
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