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• W4226346420 abstract "Abstract In this work we deal with the problem of detecting and explaining anomalous values in categorical datasets. We take the perspective of perceiving an attribute value as anomalous if its frequency is exceptional within the overall distribution of frequencies. As a first main contribution, we provide the notion of frequency occurrence . This measure can be thought of as a form of Kernel Density Estimation applied to the domain of frequency values. As a second contribution, we define an outlierness measure for categorical values that leverages the cumulated frequency distribution of the frequency occurrence distribution. This measure is able to identify two kinds of anomalies, called lower outliers and upper outliers , corresponding to exceptionally low or high frequent values. Moreover, we provide interpretable explanations for anomalous data values. We point out that providing interpretable explanations for the knowledge mined is a desirable feature of any knowledge discovery technique, though most of the traditional outlier detection methods do not provide explanations. Considering that when dealing with explanations the user could be overwhelmed by a huge amount of redundant information, as a third main contribution, we define a mechanism that allows us to single out outstanding explanations . The proposed technique is knowledge-centric , since we focus on explanation-property pairs and anomalous objects are a by-product of the mined knowledge. This clearly differentiates the proposed approach from traditional outlier detection approaches which instead are object-centric . The experiments highlight that the method is scalable and also able to identify anomalies of a different nature from those detected by traditional techniques." @default.
• W4226346420 created "2022-05-05" @default.
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• W4226346420 date "2022-04-02" @default.
• W4226346420 modified "2023-09-24" @default.
• W4226346420 title "A density estimation approach for detecting and explaining exceptional values in categorical data" @default.
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• W4226346420 doi "https://doi.org/10.1007/s10489-022-03271-3" @default.
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