基于免疫单亲遗传和模糊C均值的聚类算法

聚类算法是数据挖掘中的重要方法。为了克服FCM初始值敏感、客易陷入局部最优解以及普通遗传算法聚类时的搜索速度和聚类精度的矛盾，在分析FCM算法和基于道传聚类算法的不足基础上，提出了一种基于免疫单亲遗传和模糊C均值的混合聚类算法，先以免疫单亲遗传聚类算法初始化，找到接近全局的最优解，再用FCM算法进行求解。实验表明，它既较好地解决了局部最优问题，又可以利用FCM的优点来提高整体的收敛速度。

Clustering Algorithm Based on Evolutionary Parthian-genetic and Fuzzy C-means

Clustering algorithm is an important method in data mining.A mixed clustering algorithm based on immune genetic algorithm with fuzzy C-means(FCM) algorithm is developed after analyzing the advantages and disadvantages of fuzzy C-means algorithm and the genetic algorithm-based clustering algorithm,which is initialized by immune Partheno-genetic clustering algorithms to find results close to global optimum,and then solved by FCM.The algorithm overcomes the problem of local optimum and the contradiction of searching speed and clustering precision using standard genetic algorithm,and avoids the sensitivity of FCM to initial values.Experiments show that the proposed method can solve the locally optimum problem preferably,and improve the converge speed in virtue of the advantage of FCM algorithm.