中文摘要：

提出了一种多尺度的谱聚类算法。与传统谱聚类算法不同，多尺度谱聚类算法用改进的k-means算法对未经规范的Laplacian矩阵的特征向量进行聚类。与传统k-means算法不同，改进的k-means算法提出一种新颖的划分数据点到聚类中心的方法，通过比较聚类中心与原点的距离和引入尺度参数来计算数据点与聚类中心的距离。实验表明，改进算法在人工数据集上取得令人满意的结果，在真实数据集上聚类结果较优。

英文摘要：

A multiscale spectral clustering algorithm is proposed.Unlike the traditional spectral clustering algorithm,multiscale spectral clustering algorithm uses a modified k-means algorithm to cluster unstandardized Laplacian matrix eigenvector.Unlike the traditional k-means algorithm,the improved algorithm proposes a novel method to partition data points to cluster centers,which calculates the distance of data points and cluster centers through comparing the distance of cluster centers and origin and introducing scale parameters.Experiments show that it can acquire satisfactory results on artificial data sets,but also it can get better cluster results on real data sets.