Exploring a High-quality Outlying Feature Value Set for Noise-Resilient Outlier Detection in Categorical Data

Published: Oct 17, 2018
Abstract
Unavoidable noise in real-world categorical data presents significant challenges to existing outlier detection methods because they normally fail to separate noisy values from outlying values. Feature subspace-based methods inevitably mix noisy values when retaining an entire feature because a feature may contain both outlying values and noisy values. Pattern-based methods are normally based on frequency and are easily misled by noisy values,...
Paper Details
Title
Exploring a High-quality Outlying Feature Value Set for Noise-Resilient Outlier Detection in Categorical Data
Published Date
Oct 17, 2018
Journal
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