一种改进的密度加权的模糊C聚类算法

Improved Density Weighted Fuzzy C Means Algorithm

模糊C均值聚类算法(FCM)是一种流行的聚类算法,在许多工程领域有着广泛的应用.密度加权的模糊C均值算法(Density Weighted FCM)是对传统FCM的一种改进,它可以很好的解决FCM对噪声敏感的问题.但是DWFCM与FCM都没有解决聚类结果很大程度上依赖初始聚类中心的选择好坏的问题.提出一种基于最近邻居节点对密度的FCM改进算法Improved-DWFCM,通过最近邻居节点估计节点密度的方法解决聚类结果对初始簇中心依赖的问题.仿真结果表明这种算法选择出来的初始聚类中心与最终结果的簇中心非常接近,大大提高了算法收敛的速度以及聚类的效果.

Fuzzy C Means algoritba,a is popular soft clustering algorithm. It has been applied in many engineering fields. Density weighted FCM is its variant, which can solve FCM＇s problem： sensitive to outlier and noise data. However, performances of both algorithms are heavily depend on proper initial cluster centers. This paper proposes a novice algorithm： Improved density weighted FCM based on nearest neighbor pair and its density, simulation results show initial center produced by the algorithm are very close to final cluster center. Thus IDWFCM can convergent very quickly and imorove the Performance_