  In the past few years co-clustering has emerged as an important data mining
tool for two way data analysis. Co-clustering is more advantageous over
traditional one dimensional clustering in many ways such as, ability to find
highly correlated sub-groups of rows and columns. However, one of the
overlooked benefits of co-clustering is that, it can be used to extract
meaningful knowledge for various other knowledge extraction purposes. For
example, building predictive models with high dimensional data and
heterogeneous population is a non-trivial task. Co-clusters extracted from such
data, which shows similar pattern in both the dimension, can be used for a more
accurate predictive model building. Several applications such as finding
patient-disease cohorts in health care analysis, finding user-genre groups in
recommendation systems and community detection problems can benefit from
co-clustering technique that utilizes the predictive power of the data to
generate co-clusters for improved data analysis.
  In this paper, we present the novel idea of Predictive Overlapping
Co-Clustering (POCC) as an optimization problem for a more effective and
improved predictive analysis. Our algorithm generates optimal co-clusters by
maximizing predictive power of the co-clusters subject to the constraints on
the number of row and column clusters. In this paper precision, recall and
f-measure have been used as evaluation measures of the resulting co-clusters.
Results of our algorithm has been compared with two other well-known techniques
- K-means and Spectral co-clustering, over four real data set namely, Leukemia,
Internet-Ads, Ovarian cancer and MovieLens data set. The results demonstrate
the effectiveness and utility of our algorithm POCC in practice.
