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Voltage/Frequency Islands (VFIs) are practically used in multicore systems. VFIs cluster cores that share the same Voltage/Frequency (V/F) level for the entire application runtime. Using VFIs, switching V/F levels has less overhead per-core while saving more energy in exchange for tolerable execution delay. This paper targets the well-known K-means algorithm for clustering cores, where each cluster contains cores with similar computational workloads across applications phases. K-means produces sub-optimal clusters when cores workloads do not have the same variation across all phases. Furthermore, the workload variations require running a VFI with a different V/F level per-phase. This paper presents a fast heuristic that facilitates clustering by sorting computational workloads per-phase before applying K-means. The VFIs V/F levels are dynamically adjusted to meet energy budget constraints. This framework provides a guideline for users to choose a number of clusters that satisfies system configurations and energy efficiency preferences. Three parameters are evaluated to represent the computational workloads: utilization, Instruction-Per-Cycle (IPC), and execution time. For an application with highly varying workload, execution time achieves 52 and 65% more energy efficiency compared to utilization and IPC, respectively. Results show that the degree of workload variations for different applications impacts changing or fixing VFIs V/F levels.