  Clique clustering is the problem of partitioning the vertices of a graph into
disjoint clusters, where each cluster forms a clique in the graph, while
optimizing some objective function. In online clustering, the input graph is
given one vertex at a time, and any vertices that have previously been
clustered together are not allowed to be separated. The goal is to maintain a
clustering with an objective value close to the optimal solution. For the
variant where we want to maximize the number of edges in the clusters, we
propose an online strategy based on the doubling technique. It has an
asymptotic competitive ratio at most 15.646 and an absolute competitive ratio
at most 22.641. We also show that no deterministic strategy can have an
asymptotic competitive ratio better than 6. For the variant where we want to
minimize the number of edges between clusters, we show that the deterministic
competitive ratio of the problem is $n-\omega(1)$, where n is the number of
vertices in the graph.
