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Theoretic Outlier Detection for Large-Scale Categorical Data
March 2013 (vol. 25 no. 3) pp. 589-602 Shu Wu, Chinese Academy
of Sciences, Beijing Shengrui Wang, University of Sherbrooke,
Sherbrooke Outlier detection can usually be considered as a pre-
processing step for locating, in a data set, those objects that
do not conform to well-defined notions of expected behavior. It
is very important in data mining for discovering novel or rare
events, anomalies, vicious actions, exceptional phenomena, etc.
We are investigating outlier detection for categorical data
sets. This problem is especially challenging because of the
difficulty of defining a meaningful similarity measure for
categorical data. In this paper, we propose a formal definition
of outliers and an optimization model of outlier detection, via
a new concept of holoentropy that takes both entropy and total
correlation into consideration. Based on this model, we define a
function for the outl