通过迭代一致性聚类确定聚类数
Determining the Number of Clusters via Iterative Consensus Clustering
摘要：
我们使用聚类集合来确定一组数据中的聚类数k。一致性相似性矩阵是使用多个算法和k的多个值从集合中形成的。在一致矩阵定义的图上诱导随机游动，并使用相关转移概率矩阵的特征值来确定聚类的数量。对于有噪声或高维数据，提出了一种迭代技术，以改进这种共识矩阵的方式，鼓励块对角形式。结果表明，所得到的共识矩阵一般优于现有的相似性矩阵，这种类型的光谱分析。
英文摘要：
We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition probability matrix are used to determine the number of clusters. For noisy or high-dimensional data, an iterative technique is presented to refine this consensus matrix in way that encourages a block-diagonal form. It is shown that the resulting consensus matrix is generally superior to existing similarity matrices for this type of spectral analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
