Caching is applied to provide requested documents or Web contents quickly from a short memory. We consider the Cluster Caching Rule policy proposed recently by Markovich [12]. The idea of the rule is to keep only highly popular contents in the cache. Due to dependency in the inter-request process and heavy-tail distributed inter-request times, such frequently requested documents arise in clusters of popularity. The corresponding clusters of documents are loaded in the cache. If the requested document is present in the previous cluster, then it stays further in the cache. Otherwise, it is evicted from the cache. A mixture of m-dependent Markov and Poisson renewal processes is proposed as example of an inter-request time model. We present the hit/miss probabilities of such caching policy and consider cache size estimation.