Frameworks for multivariate \(m\)-mediods based modeling and classification in Euclidean and general feature spaces

DOI10.1016/j.patcog.2011.08.021zbMath1227.68090OpenAlexW2163119505MaRDI QIDQ650957

Shehzad Khalid, Shahid Razzaq

Publication date: 7 December 2011

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.08.021

zbMATH Keywords

classificationdata mininganomaly detectiondynamic modelingmultivariate \(m\)-mediods

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (1)

TOBAE: a density-based agglomerative clustering algorithm

Uses Software

• apcluster
• APCluster

Cites Work

• Unnamed Item
• Unnamed Item
• Clustering by Passing Messages Between Data Points
• Motion-based behaviour learning, profiling and classification in the presence of anomalies
• Learning a Mahalanobis distance metric for data clustering and classification
• Activity classification and anomaly detection using \(m\)-mediods based modelling of motion patterns

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