Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two classes of algorithms for data clustering
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans
Fuzzy Sets and Systems
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This paper is concerned with clustering of data that is partly labelled. It discusses a semi-supervised clustering algorithm based on a modified fuzzy C-Means (FCM) objective function. Semi-supervised clustering finds its application in different situations where data is neither entirely nor accurately labelled. The novelty of this approach is the fact that it takes into consideration the structure of the data and the available knowledge (labels) of patterns. The objective function consists of two components. The first concerns the unsupervised clustering while the second keeps the relationship between classes (available labels) and the clusters generated by the first component. The balance between the two components is tuned by a scaling factor. The algorithm is experimentally evaluated.