Abstract
Data visualisation can be a great support to the data mining process. We introduce a data structure that allows browsing through the data giving a complete but very manageable overview over the entire data set, where the data is split into subsets and displayed from interesting angles to reveal the relevant patterns for each subset.
Based on the features originating from principal separation analysis, a tree is grown. A node of the tree is associated with a feature and a subset of instances, and later on with a two-dimensional visualisation. At the node level, groups of instances of different classes that can be displayed from a more interesting angle are temporarily grouped together in subsets. For each of these subsets child nodes are created that display this part of the data from a more interesting angle, revealing new patterns. This process is continued until no further improved visualisation can be found.
After the tree has been constructed, it can be used to easily browse through the data. The nodes correspond with two-dimensional visualisations of the data, but the specific properties of the tree allow for three-dimensional animated transitions from one node to another, further clarifying the patterns in the data.
Partially supported by the OZR1372 project of the Vrije Universiteit Brussel.
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De Bruyne, S., Plastria, F. (2010). Multi-dimensional Data Inspection for Supervised Classification with Eigen Transformation Classification Trees. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_53
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DOI: https://doi.org/10.1007/978-3-642-15246-7_53
Publisher Name: Springer, Berlin, Heidelberg
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