Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Some Notes on Alternating Optimization
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Active semi-supervised fuzzy clustering
Pattern Recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
iTopicModel: Information Network-Integrated Topic Modeling
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Fuzzy clustering with weighted medoids for relational data
Pattern Recognition
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Fuzzy C-means based clustering for linearly and nonlinearly separable data
Pattern Recognition
Robust fuzzy clustering of relational data
IEEE Transactions on Fuzzy Systems
Fuzzy Clustering and Aggregation of Relational Data With Instance-Level Constraints
IEEE Transactions on Fuzzy Systems
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Most existing fuzzy clustering approaches group objects in a dataset based on either a feature-vector representation of each object, or pairwise relationship representation between each pair of objects. However, when both forms of data representations from different descriptions are available for a given dataset, we believe that a dual and cooperative analysis of feature-vectors (vector data) and pair-wise relationships (relational data) is likely to gain a more comprehensive understanding on the characteristics of the dataset, based on which a better clustering result may be achieved. In this paper, we develop a new fuzzy clustering approach called LinkFCM, which integrates pair-wise relationships into fuzzy c-means vector data clustering. The objective function of LinkFCM consists of two different ways to measure the compactness of clusters with respect to vector data and relational data, respectively, so that clusters are formed by utilizing these two forms of data descriptions. Our experimental study shows that LinkFCM is able to produce good clustering results for real-world document datasets by effectively making use of both content of documents and links among documents. This demonstrates the great potential of the proposed approach for data clustering, where pair-wise relationships are available together with features that describe each object.