Paper Info
Title
Constrained co-clustering with non-negative matrix factorisation
Abstract
Co-clustering refers to the problem of deriving sub-matrices of the data matrix by simultaneously clustering the rows (data instances) and columns (features) of the matrix. While very effective in discovering useful knowledge, many of the co-clustering algorithms adopt a completely unsupervised approach. Integration of domain knowledge can guide the co-clustering process and greatly enhance the overall performance. We propose a semi-supervised Non-negative Matrix-factorisation (SS-NMF) based framework to integrate domain knowledge in the form of must-link and cannot-link constraints. Specifically, we augment the data matrix by integrating the constraints using metric learning and then perform NMF to obtain co-clustering. Under the proposed framework, we present two approaches to integrate domain knowledge, viz. a distance metric learning approach and an information theoretic metric learning approach. Through experiments performed on real-world web service data and publicly available text datasets, we demonstrate the performance of the proposed SS-NMF based approach for data co-clustering.
Year
DOI
Venue
2012
10.1504/IJBIDM.2012.048728
IJBIDM
Keywords
Field
DocType
useful knowledge,real-world web service data,distance metric learning approach,non-negative matrix factorisation,information theoretic metric learning,co-clustering process,domain knowledge,data instance,unsupervised approach,co-clustering algorithm,data matrix,factorisation,co clustering,matrix,constraint,clustering
Data mining,Domain knowledge,Matrix (mathematics),Computer science,Metric (mathematics),Artificial intelligence,Constrained clustering,Non-negative matrix factorization,Biclustering,Web service,Cluster analysis,Machine learning
Journal
Volume
Issue
Citations 
7
1/2
0
PageRank 
References 
Authors
0.34
35
3
Name
Order
Citations
PageRank
Amit Salunke100.34
Xumin Liu247134.87
Manjeet Rege326417.25