  Clustering, or transitivity has been observed in real networks and its
effects on their structure and function has been discussed extensively. The
focus of these studies has been on clustering of single networks while the
effect of clustering on the robustness of coupled networks received very little
attention. Only the case of a pair of fully coupled networks with clustering
has been studied recently. Here we generalize the study of clustering of a
fully coupled pair of networks to the study of partially interdependent network
of networks with clustering within the network components. We show both
analytically and numerically, how clustering within the networks, affects the
percolation properties of interdependent networks, including percolation
threshold, size of giant component and critical coupling point where first
order phase transition changes to second order phase transition as the coupling
between the networks reduces. We study two types of clustering: one type
proposed by Newman where the average degree is kept constant while changing the
clustering and the other proposed by Hackett $et$ $al.$ where the degree
distribution is kept constant. The first type of clustering is treated both
analytically and numerically while the second one is treated only numerically.
