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中文摘要：
针对模糊c均值聚类算法自适应性不强、易陷入局部极小值及聚类效果不理想等问题，提出一种基于自适应混沌粒子群的聚类算法。对粒子群的加速因子进行动态设置，使粒子搜索机制具有自适应调节的功能；利用混沌扰动优化，使种群的多样性和全局搜索能力得到提高，利用边界缓冲墙对越界粒子进行处理，避免正负粒子飞越边界的干扰。选取 UCI机器学习库中的4种数据样本集进行测试，测试结果表明，该算法具有良好的性能。
英文摘要：
Aiming at the problem of fuzzy c‐means clustering algorithm including weak adaptability ,easy falling into local mini‐mum value and bad clustering result ,a clustering algorithm based on adaptive chaotic particle swarm optimization was proposed . Accelerating factor was dynamically set ,and particles in the search process have the adaptive adjustment function .The diversity of particle population and the global search capability were improved by utilizing the chaotic disturbance and optimization .The particles of cross‐border were treated by using the buffer boundary wall ,and the interference of positive and negative particles over the border was avoided .Finally ,the standard UCI data sets were used as the experiment data .The experimental results show that the proposed algorithm has good performance .
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