Knowledge transformation from word space to document space
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A consensus based approach to constrained clustering of software requirements
Proceedings of the 17th ACM conference on Information and knowledge management
Interactive Visualization Tools for Meta-Clustering
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Generalized cluster aggregation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Automatic malware categorization using cluster ensemble
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Data clustering with size constraints
Knowledge-Based Systems
On combining multiple clusterings: an overview and a new perspective
Applied Intelligence
Multi-task clustering via domain adaptation
Pattern Recognition
Robust nonnegative matrix factorization using L21-norm
Proceedings of the 20th ACM international conference on Information and knowledge management
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
Cluster ensembles via weighted graph regularized nonnegative matrix factorization
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Projective clustering ensembles
Data Mining and Knowledge Discovery
Semi-supervised clustering via constrained symmetric non-negative matrix factorization
BI'12 Proceedings of the 2012 international conference on Brain Informatics
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
A self-supervised framework for clustering ensemble
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
A theoretic framework of K-means-based consensus clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Ensemble clustering by means of clustering embedding in vector spaces
Pattern Recognition
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Consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. Consensus clustering (also known as aggregation of clustering) can improve clustering robustness, deal with distributed and heterogeneous data sources and make use of multiple clustering criteria. Semi-supervised clustering can integrate various forms of background knowledge into clustering. In this paper, we show how consensus and semi-supervised clustering can be formulated within the framework of nonnegative matrix factorization (NMF). We show that this framework yields NMF-based algorithms that are: (1) extremely simple to implement; (2) provably correct and provably convergent. We conduct a wide range of comparative experiments that demonstrate the effectiveness of this NMF-based approach.