Jadson Castro Gertrudes
Arthur Zimek
Jörg Sander
ABSTRACT
Semi-supervised classification is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatically increasing. In this paper, we introduce a unified framework for semi-supervised classification based on building-blocks from density-based clustering. This framework is not only efficient and effective, but it is also statistically sound. Experimental results on a large collection of datasets show the advantages of the proposed framework.
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Index Terms
- A unified framework of density-based clustering for semi-supervised classification
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