Abstract: Against such shortcomings of the traditional fish-swarm algorithm with the effect of initial fish-group random, easily falling into a local extremum, low efficiency of handling boundary data and low clustering accuracy, an improved artificial fish swarm algorithm based on Rough set and granular computing is proposed. Initially, the algorithm introduces the granular computing theory and initializes the fish group by the density and max-min distance means so that the algorithm avoids being effected by random. Meantime the algorithm is combined with rough set and attribute reduction and decision system to resolve the clustering problem of boundary data. With the principles of within-class compactness and between-class separability while designing the fitness function, it also can be regarded as the termination condition of algorithm. Experiment results show that the algorithm has enhanced the accuracy and the abilities to obtain global extremum and embodies better clustering performance.

Key words: clustering, granular computing, rough set, attribute reduction

摘要： 针对标准鱼群算法易受到初始鱼群随机性的影响，后期收敛速度减慢，处理边界数据能力低，聚类精度低等缺点，提出了基于粒计算与粗糙集的人工鱼群聚类算法。算法引入粒计算理论，并依据粒密度和最大最小距离积法选择初始化人工鱼群避免算法易受随机性的影响；通过结合粗糙集的决策系统和属性约简，提高算法解决边界数据的能力；采用类内紧致性和类间分离度的原则设计适应度函数，并将其作为算法的终止判断条件。实验结果表明：该算法提高了聚类精度，增强了获取全局极值的能力，具有良好的聚类效果。

关键词: 聚类, 粒计算, 粗糙集, 属性约简