VNSOptClust: A Variable Neighborhood Search Based Approach for Unsupervised Anomaly Detection

DOI10.1007/978-3-540-87477-5_64zbMath1160.90709OpenAlexW1491651839MaRDI QIDQ3627769

Nabil Belacel, Christa Wang

Publication date: 13 May 2009

Published in: Communications in Computer and Information Science (Search for Journal in Brave)

Full work available at URL: https://nrc-publications.canada.ca/eng/view/accepted/?id=cd4f2c5e-f49d-4c89-a0ca-992a0d72edcd

zbMATH Keywords

unsupervised learningvariable neighborhood searchautomatic partitional clusteringunsupervised anomaly detection

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59)

Uses Software

• J-MEANS
• Silhouettes

Cites Work

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• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Fuzzy J-means: a new heuristic for fuzzy clustering
• Variable neighborhood search for the p-median
• A near-optimal initial seed value selection in K-means means algorithm using a genetic algorithm
• Variable Neighborhood Search
• J-MEANS: A new local search heuristic for minimum sum of squares clustering
• A genetic approach to the automatic clustering problem