A Method of Two Stage Clustering Using Agglomerative Hierarchical Algorithms with One-Pass k-Means++ or k-Median++
A Method of Two Stage Clustering Using Agglomerative Hierarchical Algorithms with One-Pass k-Means++ or k-Median++
复制标题
一种使用一次性 k 均值或 k 中值的凝聚层次算法的两阶段聚类方法
DOI：
10.1109/grc.2014.6982834
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Sadaaki Miyamoto
中科院分区：
文献类型：
--
作者：
Yusuke Tamura;Sadaaki Miyamoto
近年来聚类研究的重点是基于图结构的聚类生成。给出具有权重的节点和边，并找到具有密集节点群的聚类。为此提出了几种算法，其中常用的是谱聚类和DBSCAN方法。前者采用特征值分析，后者采用核心点的概念，基于密度搜索。本研究旨在将两种算法结合起来减少计算量，同时利用两种方法的优点。包括这两种算法在内的一组算法的关系揭示了算法的本质，并给出了包括这些算法以及其他传统算法的方法学视角。因此，我们提出了一种结合这类算法思想的高效算法。理论和数值算例表明了该算法的有效性和高效性。
Recent attention in studies of clustering is focused upon generation of clusters on the basis of graph structures. Nodes and edges with weights are given and clusters with dense groups of nodes should be found. Several algorithms have been proposed for this purpose, among which the method of spectral clustering and DBSCAN have frequently been used. The former uses eigenvalue analysis while the latter is based on density seeking using the concept of core points. This study aims at combining the two algorithms to reduce computation and at the same time using advantages of the both methods. Relations of a family of algorithms including these two uncovers the nature of the algorithms and gives a methodological perspective including these algorithms as well as other traditional algorithms. As a result we propose an efficient algorithm combining the ideas of this family of algorithms. The effectiveness and efficiency of the proposed algorithm are shown theoretically and by numerical examples.