Density Difference Detection with Application to Exploratory Visualization
Identifying differences among the distribution of samples of different observations is an important issue in many research fields. We provide a general framework to detect these difference spots in d-dimensional feature space. Such spots occur not only at various locations, they may also come in various shapes and multiple sizes, even at the same location. We address these challenges by a scale-space representation of the density function difference of the observations in feature space. Using three classification scenarios from UCI Machine Learning Repository we show that interest spots carry valuable information about a data set. To this end, we establish a simple decision rule on top of our framework. Results indicate state-of-the-art performance, underpinning the importance of the information that is carried by the detected spots. Furthermore, we outline that the output of our framework can be used to guide exploratory visualization of high-dimensional feature spaces.
KeywordsDensity difference Kernel density estimation Scale space Dendrogram Blob detection Affine shape adaption Exploratory visualization Orthographic star coordinates
This research was partially funded by the project “Visual Analytics in Public Health” (TO 166/13-2) of the German Research Foundation.
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