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Clustering and scheduling of tasks for parallel implementation is a well researched problem. Several techniques have been presented in the literature to improve performance and reduce problem execution times. In this paper we present a novel approach where clustering and scheduling of tasks can be tuned to achieve maximal speedup or efficiency. The proposed scheme is based on the relation between the costs of computation and communication of task clusters. In this paper, we show how clustering can be adapted to suit different architectures and number of available processors. The proposed efficient clustering and scheduling algorithm is flexible : the clustering and scheduling can be tuned to suit bounded or unbounded number of processors and/or parallel computing environment. Comparative studies indicate superior efficiency compared to most other schemes proposed in recent years.