6533b86ffe1ef96bd12ce59c
RESEARCH PRODUCT
An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering
Enrique VidalRamón Alberto MollinedaFrancesc J. Ferrisubject
Single-linkage clusteringcomputer.software_genreComplete-linkage clusteringHierarchical clusteringk-nearest neighbors algorithmArtificial IntelligenceNearest-neighbor chain algorithmClassification ruleSignal ProcessingCluster (physics)Computer Vision and Pattern RecognitionData miningMerge (version control)computerSoftwareMathematicsdescription
Abstract A generalized prototype-based classification scheme founded on hierarchical clustering is proposed. The basic idea is to obtain a condensed 1-NN classification rule by merging the two same-class nearest clusters, provided that the set of cluster representatives correctly classifies all the original points. Apart from the quality of the obtained sets and its flexibility which comes from the fact that different intercluster measures and criteria can be used, the proposed scheme includes a very efficient four-stage procedure which conveniently exploits geometric cluster properties to decide about each possible merge. Empirical results demonstrate the merits of the proposed algorithm taking into account the size of the condensed sets of prototypes, the accuracy of the corresponding condensed 1-NN classification rule and the computing time.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2002-12-01 | Pattern Recognition |