Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
Affinity Propagation Clustering with Incomplete Data
verfasst von : Cheng Lu, Shiji Song, Cheng Wu
Erschienen in: Computational Intelligence, Networked Systems and Their Applications
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Incomplete data are often encountered in data sets for clustering problems, and inappropriate treatment of incomplete data will significantly degrade the clustering performances. The Affinity Propagation (AP) algorithm is an effective algorithm for clustering analysis, but it is not directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward improved AP clustering for solving incomplete data problems. Three strategies(WDS, PDS and IPDS) are given, which involve modified versions of the AP algorithm. Clustering performances at different missing rates are discussed, and all approaches are tested on several UCI data sets with randomly missing data.