Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering

@inproceedings{Basu2003ComparingAU,
  title={Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering},
  author={Sugato Basu},
  year={2003}
}
Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has employed one of two approaches: 1) Searchbased methods that utilize supervised data to guide the search for the best clustering, and 2) Similarity-based methods that use supervised data to adapt the underlying similarity metric used by the clustering algorithm. This paper presents a unified approach based on the K-Means clustering algorithm that incorporates both of… CONTINUE READING
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