Traditional incremental clustering algorithm needs to be set parameters and cannot deal with the imbalance data.To solve the problem, the incremental clustering algorithm of bayesian adaptive resonance theory based on local distribution is proposed. Firstly, the new data are collected by data snapshots. Then, in current data snapshot, the new data are clustered into the winning cluster adaptively according to the local distribution of the clusters without predefined parameters. Then, the evolving relationships between the clusters in two neighboring data snapshots are determined. Finally, the simulation result shows that the proposed algorithm can improve the accuracy and the adaptability.