Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent Dirichlet Co-Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative filtering using orthogonal nonnegative matrix tri-factorization
Information Processing and Management: an International Journal
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Clustering plays an important role in data mining as many applications use it as a preprocessing step for data analysis. Traditional clustering focuses on the grouping of similar objects, while two-way coclustering can group dyadic data (objects as well as their attributes) simultaneously. Most co-clustering research focuses on single correlation data, but there might be other possible descriptions of dyadic data that could improve co-clustering performance. In this research, we extend ITCC (Information Theoretic Co-Clustering) to the problem of coclustering with augmented matrix. We proposed CCAM (Co-Clustering with Augmented Data Matrix) to include this augmented data for better co-clustering. We apply CCAM in the analysis of on-line advertising, where both ads and users must be clustered. The key data that connect ads and users are the user-ad link matrix, which identifies the ads that each user has linked; both ads and users also have their feature data, i.e. the augmented data matrix. To evaluate the proposed method, we use two measures: classification accuracy and K-L divergence. The experiment is done using the advertisements and user data from Morgenstern, a financial social website that focuses on the advertisement agency. The experiment results show that CCAM provides better performance than ITCC since it consider the use of augmented data during clustering.