Abstract A new Hybrid Clustering Algorithm (HCA) that incorporates the fuzzy C-means into the canonical genetic algorithm is proposed in this paper. The HCA speeds up convergence before the genetic algorithm reach the global optima, and eliminates fuzzy C-means trapped local minima by performing global search and local search alternatively. The experiments for clustering three data sets with different distributions show that the HCA has better generalization and effectiveness.