Semi-supervised outlier detection based on fuzzy rough \(c\)-means clustering

DOI10.1016/J.MATCOM.2010.02.007zbMath1191.62115OpenAlexW1982418571MaRDI QIDQ982921

Zhenxia Xue, Aifen Feng, You-lin Shang

Publication date: 28 July 2010

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2010.02.007

zbMATH Keywords

rough setsfuzzy setspattern recognitionoutlier detectionsemi-supervised learning\(c\)-means clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Multivariate analysis and fuzziness (62H86)

Related Items (7)

A LoOP based outlier detection method for high dimensional fuzzy data set ⋮ Recent fuzzy generalisations of rough sets theory: a systematic review and methodological critique of the literature ⋮ On the three-component mixture of exponential distributions: a Bayesian framework to model data with multiple lower and upper outliers ⋮ Possibilistic exponential fuzzy clustering ⋮ Improved roughk-means clustering algorithm based on weighted distance measure with Gaussian function ⋮ An improved semisupervised outlier detection algorithm based on adaptive feature weighted clustering ⋮ Fast pedestrian detection system with a two layer cascade of classifiers

Uses Software

• LOF

Cites Work

• Interval set clustering of web users with rough \(K\)-means
• Some Recent Investigations of a New Fuzzy Partitioning Algorithm and its Application to Pattern Classification Problems
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