Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Semi-supervised learning with explicit misclassification modeling
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Some Pairwise Constrained Semi-Supervised Fuzzy c-Means Clustering Algorithms
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
A novel fuzzy kernel C-means algorithm for document clustering
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Background knowledge integration in clustering using purity indexes
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Semi-supervised clustering with discriminative random fields
Pattern Recognition
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Clustering with partial supervision finds its application in situations where data is neither entirely nor accurately labeled. This paper discusses a semi-supervised clustering algorithm based on a modified version of the fuzzy C-Means (FCM) algorithm. The objective function of the proposed algorithm consists of two components. The first concerns traditional unsupervised clustering while the second tracks 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. Comprehensive experimental studies are presented. First, the discrimination of the proposed algorithm is discussed before its reformulation as a classifier is addressed. The induced classifier is evaluated on completely labeled data and validated by comparison against some fully supervised classifiers, namely support vector machines and neural networks. This classifier is then evaluated and compared against three semi-supervised algorithms in the context of learning from partly labeled data. In addition, the behavior of the algorithm is discussed and the relation between classes and clusters is investigated using a linear regression model. Finally, the complexity of the algorithm is briefly discussed.