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

Chien-Liang Liu*, Tao Hsing Chang, Hsuan Hsun Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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 similarity 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.

Original languageEnglish
Pages (from-to)48-64
Number of pages17
JournalFuzzy Sets and Systems
StatePublished - 5 Feb 2013


  • Fuzzy clustering
  • Fuzzy semi-Kmeans
  • Semi-supervised learning
  • Text mining


Dive into the research topics of 'Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans'. Together they form a unique fingerprint.

Cite this