ACM Computing Surveys (CSUR)
Concept decompositions for large sparse text data using clustering
Machine Learning
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
A new greedy algorithm for improving b-coloring clustering
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Semi-Supervised Learning
Performance evaluation of constraints in graph-based semi-supervised clustering
AMT'10 Proceedings of the 6th international conference on Active media technology
Pairwise constraint propagation for graph-based semi-supervised clustering
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Influence of erroneous pairwise constraints in semi-supervised clustering
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
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This paper proposes a graph-based projection approach for semi-supervised clustering based on pairwise relations among instances. In our approach, the entire data is represented as an edge-weighted graph with the pairwise similarities among instances. Graph representation enables to deal with two kinds of pairwise constraints as well as pairwise similarities over the same unified representation. Then, in order to reflect the pairwise constraints on the clustering process, the graph is modified by contraction in graph theory and graph Laplacian in spectral graph theory. By exploiting the constraints as well as similarities among instances, the entire data are projected onto a subspace via the modified graph, and data clustering is conducted over the projected representation. The proposed approach is evaluated over several real world datasets. The results are encouraging and indicate the effectiveness of the proposed approach.