Clustering by scale-space filtering
Clustering by scale-space filtering
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DOI：
10.1109/34.895974
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发表时间：
2000-12-01
影响因子：
23.6
通讯作者：
Xu, ZB
中科院分区：
文献类型：
--
作者：
Leung, Y;Zhang, JS;Xu, ZB
在模式识别和图像处理方面。作为聚类分析的主要应用领域，人眼似乎具有将对象分组并以高效和有效的方式找到重要结构的独特能力。因此，模拟视觉系统的聚类算法可以解决这些研究领域中的一些基本问题。从这个角度来看，我们提出了一个新的方法来数据聚类建模的基础上的尺度空间理论的横向视网膜互连的模糊效应。在这种方法中，数据集被认为是一个图像，每个光点位于一个基准位置。当我们模糊这个图像时，较小的光团合并成较大的光团，直到整个图像在足够低的分辨率下变成一个光团。通过用聚类来标识每个斑点，模糊处理沿着层次生成聚类族。所提出的方法的优点是：1）派生的算法是计算稳定和不敏感的初始化，他们是完全摆脱解决困难的全局优化问题。2)它有利于构建新的检查集群的有效性，并提供了最终的聚类数据中的噪声和规模的变化显着程度的鲁棒性。3)它是更强大的情况下，超椭球分区可能不会被假定。4)它适合于在聚类过程中保持离群值的结构和完整性的任务。5)聚类与人眼感知的聚类高度一致。6)新的方法提供了一个统一的框架尺度相关的聚类算法最近来自许多不同的领域，如估计理论，经常性的信号处理自组织特征映射，信息论和统计力学，径向基函数神经网络。
In pattern recognition and image processing. the major application areas of cluster analysis, human eyes seem to possess a singular aptitude to group objects and find important structures in an efficient and effective way. Thus, a clustering algorithm simulating a visual system may solve some basic problems in these areas of research. From this point of view, we propose a new approach to data clustering by modeling the blurring effect of lateral retinal interconnections based on scale space theory. In this approach, a data set is considered as an image with each light point located at a datum position. As we blur this image, smaller light blobs merge into larger ones until the whole image becomes one light blob at a low enough level of resolution. By identifying each blob with a cluster, the blurring process generates a family of clusterings along the hierarchy. The advantages of the proposed approach are: 1) The derived algorithms are computationally stable and insensitive to initialization and they are totally free from solving difficult global optimization problems. 2) It facilitates the construction of new checks on cluster validity and provides the final clustering a significant degree of robustness to noise in data and change in scale. 3) It is more robust in cases where hyperellipsoidal partitions may not be assumed. 4) It is suitable for the task of preserving the structure and integrity of the outliers in the clustering process. 5) The clustering is highly consistent with that perceived by human eyes. 6) The new approach provides a unified framework for scale-related clustering algorithms recently derived from many different fields such as estimation theory, recurrent signal processing on selforganization feature maps, information theory and statistical mechanics, and radial basis function neural networks.