Algorithms for frequent pattern mining, a popular informatics application, have unique requirements that are not met by any of the existing parallel tools. In particular, such applications operate on extremely large data sets and have irregular memory access patterns. For efficient parallelization of such applications, it is necessary to support dynamic load balancing along with scheduling mechanisms that allow users to exploit data locality. Given these requirements, task parallelism is the most promising of the available parallel programming models. However, existing solutions for task parallelism schedule tasks implicitly and hence, custom scheduling policies that can exploit data locality cannot be easily employed. In this paper we demonstrate and characterize the speedup obtained in a frequent pattern mining application using a custom clustered scheduling policy in place of the popular Cilk-style policy. We present PFunc, a novel task parallel library whose customizable task scheduling and task priorities facilitated the implementation of our clustered scheduling policy.