We strongly believe that the current Utility Itemset Mining (UIM) problem model can be extended with a key modeling capability of predicting future itemsets based on prior knowledge of clusters in the dataset. Information in transactions fairly representative of a cluster type is more a characteristic of the cluster type than the the entire data. Subjecting such transactions to the common threshold in the UIM problem leads to information loss. We identify that an implicit use of the cluster structure of data in the UIM problem model will address this limitation. We achieve this by introducing a new clustering based utility in the definition of the UIM problem model and modifying the definitions of absolute utilities based on it. This enhances the UIM model by including a predictive aspect to it, thereby enabling the cluster specific patterns to emerge while still mining the inter-cluster patterns. By performing experiments on two real data sets we are able to verify that our proposed predictive UIM problem model extracts more useful information than the current UIM model with high accuracy.