Efficient editing and data abstraction by finding homogeneous clusters

DOI10.1007/S10472-015-9472-8zbMath1356.68169OpenAlexW2136337904MaRDI QIDQ287759

Stefanos Ougiaroglou, Georgios Evangelidis

Publication date: 23 May 2016

Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10472-015-9472-8

zbMATH Keywords

noiseclusteringdata reductionprototypesdata abstraction\(k\)-NN classificationediting

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Uses Software

• JStatCom
• KEEL

Cites Work

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• A memetic algorithm for evolutionary prototype selection: A scaling up approach
• Reduction techniques for instance-based learning algorithms
• Advances in K-means Clustering
• The Probability Distribution of Conditional Classification Error
• An Experiment with the Edited Nearest-Neighbor Rule
• Non-constructive Galois-Tukey connections
• Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
• EHC: Non-parametric Editing by Finding Homogeneous Clusters
• Nearest neighbor pattern classification

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