Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans

Chien-Liang Liu, Tao-Hsing Chang, Hsuan-Hsun Li
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
more » ... y function can be used as well. This work conducts experiments on three data sets and compares fuzzy semi-Kmeans with several methods. The experimental results indicate that fuzzy semi-Kmeans can generally outperform the other methods.
doi:10.1016/j.fss.2013.01.004 fatcat:5qgbucjr4fcxvhffxdgdqbi4gq