A Novel Density-Based Clustering Framework by Using Level Set Method
A Novel Density-Based Clustering Framework by Using Level Set Method
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DOI：
10.1109/tkde.2009.21
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发表时间：
2009-11
影响因子：
8.9
通讯作者：
Xiaofeng Wang;De-shuang Huang
中科院分区：
文献类型：
--
作者：
Xiaofeng Wang;De-shuang Huang
本文提出了一种新的基于密度的聚类框架，采用的假设，在数据空间中的聚类中心可以看作是在图像空间中的目标对象。首先，采用水平集演化方法，通过一种新的初始边界形成方案，找到了一个近似的聚类中心。相应地，三种类型的初始边界被定义，使得它们中的每一个可以以不同的方式进化以接近聚类中心。针对数据空间中水平集演化迭代时间长的问题，提出了一种有效的终止准则，在无法找到聚类中心的情况下终止演化过程，并根据演化结果构造了一种新的有效密度表示--水平集密度（LSD）.最后，基于最小二乘方差，利用谷值搜索聚类将数据点划分到相应的类中。在人工数据集和真实的数据集上的实验表明了该聚类框架的有效性。与DBSCAN方法、OPTICS方法和谷值搜索聚类方法的比较进一步表明，该框架可以成功避免过拟合现象，解决聚类边界点和离群点的混淆问题。
In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data space can be regarded as target objects in image space. First, the level set evolution is adopted to find an approximation of cluster centers by using a new initial boundary formation scheme. Accordingly, three types of initial boundaries are defined so that each of them can evolve to approach the cluster centers in different ways. To avoid the long iteration time of level set evolution in data space, an efficient termination criterion is presented to stop the evolution process in the circumstance that no more cluster centers can be found. Then, a new effective density representation called level set density (LSD) is constructed from the evolution results. Finally, the valley seeking clustering is used to group data points into corresponding clusters based on the LSD. The experiments on some synthetic and real data sets have demonstrated the efficiency and effectiveness of the proposed clustering framework. The comparisons with DBSCAN method, OPTICS method, and valley seeking clustering method further show that the proposed framework can successfully avoid the overfitting phenomenon and solve the confusion problem of cluster boundary points and outliers.