Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
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Normalized Cuts and Image Segmentation
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Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
A probabilistic framework for semi-supervised clustering
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Supervised clustering with support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
Document clustering with prior knowledge
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Semi-supervised model-based document clustering: A comparative study
Machine Learning
Introduction to Information Retrieval
Introduction to Information Retrieval
A systematic analysis of performance measures for classification tasks
Information Processing and Management: an International Journal
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Partially supervised clustering for image segmentation
Pattern Recognition
A semi-supervised clustering algorithm for data exploration
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Text document clustering with metric learning
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Flexible constrained spectral clustering
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Locality sensitive C-means clustering algorithms
Neurocomputing
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GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.