基于多尺度信息融合的层次聚类算法

Hierarchical Clustering Based on Multi-scale Information Fusion

在体绘制领域和图像分割中,数据集通常具有流形结构,各部分边界连接紧密且伴随局部噪声,给传统聚类算法的应用带来了较大的困难.本文根据非参数密度估计方法提出了一种基于多尺度信息融合的层次聚类算法.新算法通过整合密度差异和边界信息构造了一种多尺度结构信息融合的相似性度量,通过水平集的图连接策略推导出一种层次化的类结构剖析过程以获取稳定的聚类结果.新算法不受数据集形状、密度类型的限制,无需对数据集进行假设,可自动识别数据集常见的聚类结构特征.同时聚类结果较为稳定,算法对噪声具有较强的鲁棒性.从人工数据集和真实数据集以及应用试验的测试结果可以看出新算法的优越性能.

In volume rendering and image segmentation, data set often possesses manifold structure. Different parts in such data sets are closely adjacent to each other and local noises exist around the boundaries, which bring great difficulty to traditional clustering algorithms. According to the non-parameter density estimation, this paper proposes a hierarchical clustering algorithm based on multi-scale information fusion. The new algorithm integrates density differences and boundary information to define a kind of similarity measurement based on multiscale information fusion. With the graph connection of level sets, the new approach obtains a hierarchical analyzing process of the cluster structures, which outputs stable clustering results. The new algorithm is not restricted by shapes and density structures of the data set, and can detect common structural features of the data set automatically without assumption. Meanwhile, the clustering results are stable and the new algorithm is strongly robust to noises. The superiority of the proposed algorithm is demonstrated with its applications to synthetic and real data sets.