邻域信息加权的最小二乘投影双支持向量聚类

Weighted least square projection twin support vector clustering with neighborhood information

针对最小二乘投影双支持向量聚类(LSPTSVC)算法未充分利用样本邻域之间的潜在信息、实用性不强等问题,本文提出了一种高效的邻域信息加权的最小二乘投影双支持向量聚类算法。首先引入相对密度概念充分提取数据集中同类数据点之间的局部相似性信息,然后计算该点的相对权重,最后利用该权重获得数据点的加权平均值,来更好的反映同类样本的几何结构。实验结果验证了所提算法的有效性,结果表明本文算法在相似的计算复杂度下,相比现有方法取得了更好的聚类准确性,且在真实世界的医学数据集的实际应用中表现出良好的聚类性能。

In order to solve the problem that the least square projection twin support vector clustering(LSPTSVC)algorithm fails to make full use of the potential information among sample neighborhoods and is not practical,this paper proposes an efficient weighted least square projection twin support vector clustering algorithm with neighborhood information.Firstly,the algorithm introduces the concept of relative density to fully extract local similarity information between data points of the same class.Then,the algorithm calculates the relative weight of the point.Finally,in order to better reflect the geometric structure of similar samples,the algorithm calculates the weighted average value of the data points by using the relative weight.These experimental results verify the effectiveness of the algorithm.The results show that the proposed algorithm achieves better clustering accuracy than the existing methods under the similar computational complexity and good clustering performance in the practical application of medical datasets in the real world.