2011 | OriginalPaper | Buchkapitel
Semi–supervised K-Means Clustering by Optimizing Initial Cluster Centers
verfasst von : Xin Wang, Chaofei Wang, Junyi Shen
Erschienen in: Web Information Systems and Mining
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
Semi-supervised clustering uses a small amount of labeled data to aid and bias the clustering of unlabeled data. This paper explores the usage of labeled data to generate and optimize initial cluster centers for k-means algorithm. It proposes a max-distance search approach in order to find some optimal initial cluster centers from unlabeled data, especially when labeled data can’t provide enough initial cluster centers. Experimental results demonstrate the advantages of this method over standard random selection and partial random selection, in which some initial cluster centers come from labeled data while the other come from unlabeled data by random selection.