6533b857fe1ef96bd12b4d9b

RESEARCH PRODUCT

Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm

Francesc J. FerriKrzysztof Rzaḑca

subject

Clustering high-dimensional datak-medoidsComputer scienceCURE data clustering algorithmSingle-linkage clusteringCanopy clustering algorithmVariance (accounting)Data miningCluster analysiscomputer.software_genrecomputerk-medians clustering

description

A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the benefits of the plain clustering algorithm with regard to other approaches for clustering. Experiments using both synthetic and real data have been performed in order to evaluate the differences between the proposed methodology and the plain use of the Maximum Variance algorithm. According to the results obtained, the proposal constitutes an efficient and accurate alternative.

yearjournalcountryeditionlanguage
2003-01-01
https://doi.org/10.1007/978-3-540-44871-6_100