Dynamic cluster formation using level set methods.
Dynamic cluster formation using level set methods.
复制标题
使用水平集方法动态聚类形成。
DOI：
10.1109/tpami.2006.117
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Chan,TonyF
中科院分区：
文献类型：
--
作者：
Yip,AndyM;Ding,Chris;Chan,TonyF
基于密度的聚类具有以下优点：1）允许任意形状的聚类，以及2）不需要聚类的数量作为输入。然而，当聚类彼此接触时，聚类中心和聚类边界（作为密度分布的峰和谷）变得模糊并且难以确定。我们引入了集群强度函数（CIF）的概念，它捕捉了集群的重要特征。当簇被很好地分离时，CIF类似于密度函数。但是，当聚类变得彼此接近时，CIF仍然清楚地显示聚类中心，聚类边界以及每个数据点对其所属聚类的隶属度。基于这些函数的凸点搜索和谷点搜索聚类比基于核密度估计得到的密度函数的聚类更鲁棒，而核密度估计得到的密度函数往往是振荡的或过平滑的。这些问题的核密度估计解决水平集方法和相关技术。与现有的两种基于密度的方法，谷寻求和DBSCAN，比较，说明了我们的方法的优势。
Density-based clustering has the advantages for: 1) allowing arbitrary shape of cluster and 2) not requiring the number of clusters as input. However, when clusters touch each other, both the cluster centers and cluster boundaries (as the peaks and valleys of the density distribution) become fuzzy and difficult to determine. We introduce the notion of cluster intensity function (CIF) which captures the important characteristics of clusters. When clusters are well-separated, CIFs are similar to density functions. But, when clusters become closed to each other, CIFs still clearly reveal cluster centers, cluster boundaries, and degree of membership of each data point to the cluster that it belongs. Clustering through bump hunting and valley seeking based on these functions are more robust than that based on density functions obtained by kernel density estimation, which are often oscillatory or oversmoothed. These problems of kernel density estimation are resolved using level set methods and related techniques. Comparisons with two existing density-based methods, valley seeking and DBSCAN, are presented which illustrate the advantages of our approach.