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Clustering techniques organize a collection of objects into cohesive groups called clusters such that objects in the same cluster are more similar to each other than objects in different clusters. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Combining multiple clustering is an approach to overcome the deficiency of single algorithms and further enhance their performances. Current approaches to combining multiple clusterings use end-result cooperation (e.g. ensemble clustering and hybrid clustering) between the clustering algorithms. Inherent drawbacks of the end-result cooperation are: the computational complexity of ensemble clustering and the idle wasted time in the hybrid approaches. In this paper, the k-means and the bisecting k-means clustering algorithms are both combined using intermediate-cooperation strategy for the aim of obtaining better clustering solutions than non-cooperative algorithms. Undertaken experimental results show that the quality of the clustering solutions obtained from the cooperative partitional-divisive clustering (CPDC) model is better than those obtained from the non-cooperative algorithms over a number of gene expression datasets.