Weighted Clustering
Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker
Abstract
We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights. We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify algorithms accordingly.
Topics & Concepts
Cluster analysisComputer scienceCorrelation clusteringCURE data clustering algorithmGeneralizationSingle-linkage clusteringData miningCanopy clustering algorithmArtificial intelligenceFuzzy clusteringConsensus clusteringHierarchical clusteringSelection (genetic algorithm)Pattern recognition (psychology)MathematicsMathematical analysisAdvanced Clustering Algorithms ResearchRough Sets and Fuzzy LogicData Management and Algorithms