摘要
K均值聚类算法是一种经典的数据挖掘算法,但该算法存在对初始值敏感且容易陷入局部最优问题,一定程度上影响分类结果的准确性。通过分析蚁群算法和粒子群算法,将两者混合算法应用到K均值聚类算法,提出一种K均值聚类优化算法。仿真结果表明,该优化算法不易受到初始值取值的影响,且具有较强的全局寻优能力,可作为聚类分析的一种有效方法。
K means clustering algorithm is a classic data mining method, but the algorithm is sensitive to initial value and easy to fall into local optimum problem. The accuracy of the classification results are affected to a certain extent. Through the analysis of ant colony algorithm （ACA） and particle swanal algorithm （PSO）, the hybrid algorithm is appfied to the K - means algorithm, we propose an improved K - means algorithm. The simulation results show that the improved algorithm is not easily affected by the initial value of the influence, and has the strong ability of global optimization. It is an effective method of cluster analysis.
出处
《数字技术与应用》
2015年第4期122-123,共2页
Digital Technology & Application
关键词
蚁群算法
粒子群算法
K均值聚类算法
ant colony algorithm
particle swama algorithm
K - means clustering algorithm
