document: A framework for simultaneous co-clustering and learning from complex data For difficult classification or regression problems , practitioners often segment the data into relatively homogenous groups and then build a model for each group . This two-step procedure usually results in simpler , more interpretable and actionable models without any lossin accuracy . We consider problems such as predicting customer behavior across products , where the independent variables can be naturally partitioned into two groups . A pivoting operation can now result in the dependent variable showing up as entries in a `` customer by product '' data matrix . We present a model-based co-clustering ( meta ) - algorithm that interleaves clustering and construction of prediction models to iteratively improve both cluster assignment and fit of the models . This algorithm provably converges to a local minimum of a suitable cost function . The framework not only generalizes co-clustering and collaborative filtering to model-basedco-clustering , but can also be viewed as simultaneous co-segmentation and classification or regression , which is better than independently clustering the data first and then building models . Moreover , it applies to a wide range of bi-modal or multimodal data , and can be easily specialized to address classification and regression problems . We demonstrate the effectiveness of our approach on both these problems through experimentation on real and synthetic data .
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