基于免疫遗传的K-Means聚类算法分析

Clustering analysis based on K-means of immune genetic algorithm

聚类算法是数据挖掘中的一个重要研究领域,在所有的聚类算法中K-Means算法应用得最为广泛。针对K-Means算法容易陷入局部最优解的缺点,提出了基于免疫遗传的K-Means聚类算法来避免这个问题。理论分析和实验表明,该算法比传统的K-Means聚类有更好的效果。

Clustering algorithms analysis is an important area in data ruing, among all the clustering algorithm, K-means clustering is widely used. But in practical applications, it easily plunges into the local optimum. A cluster method based on K-means of immune genetic algorithm is adopted to avoid the defect. Theoretic analyses and experiments show that the result of this algorithm is better than those which only using the tradition＇s K-means algorithm.