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Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans
2013
Fuzzy sets and systems (Print)
While focusing on document clustering, this work presents a fuzzy semi-supervised clustering algorithm called fuzzy semi-Kmeans. The fuzzy semi-Kmeans is an extension of K-means clustering model, and it is inspired by an EM algorithm and a Gaussian mixture model. Additionally, the fuzzy semi-Kmeans provides the flexibility to employ different fuzzy membership functions to measure the distance between data. This work employs Gaussian weighting function to conduct experiments, but cosine
doi:10.1016/j.fss.2013.01.004
fatcat:5qgbucjr4fcxvhffxdgdqbi4gq