Our previous experiments demonstrated that subsets collections of (short)
documents (with several hundred entries) share a common normalized in some way
eigenvalue spectrum of combinatorial Laplacian. Based on this insight, we
propose a method of incremental spectral clustering. The method consists of the
following steps: (1) split the data into manageable subsets, (2) cluster each
of the subsets, (3) merge clusters from different subsets based on the
eigenvalue spectrum similarity to form clusters of the entire set. This method
can be especially useful for clustering methods of complexity strongly
increasing with the size of the data sample,like in case of typical spectral
clustering. Experiments were performed showing that in fact the clustering and
merging the subsets yields clusters close to clustering the entire dataset.

基于子集集合的归一化特征值谱，我们提出了一种增量谱聚类方法，将数据分成可管理的子集，对每个子集进行聚类，根据特征值谱的相似性合并不同子集的聚类结果，从而实现整个数据集的聚类。实验结果表明，该方法能够获得接近整个数据集聚类结果的子集聚类和合并。