In numerous applications the rapid increase of data size with time makes classical clustering algorithms too slow because of the high computational cost. In the present contribution, a novel method for online spectral clustering algorithm is introduced, which can be applied for simulation of structure and properties of titanate in chemical engineering. The proposed algorithm uses the recent results in the emerging field of graph signal processing. It avoids the costly computation of the eigenvectors by filtering random signals on the graph, and only a few derivative operations are needed to update the clustering result when a new data arrives. Initial simulations are presented using both simulated and real data sets, illustrating the relevance of the proposed algorithm.

中文翻译：

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通过图傅立叶变换在钛酸盐应用中进行快速增量谱聚类

在许多应用中，数据大小随时间的快速增长使得经典聚类算法由于计算成本高而变得太慢。在目前的贡献中，介绍了一种在线光谱聚类算法的新方法，该方法可用于化学工程中钛酸盐的结构和性质的模拟。所提出的算法使用了图信号处理新兴领域的最新成果。它通过过滤图上的随机信号避免了代价高昂的特征向量计算，并且在新数据到来时只需要进行少量的导数运算即可更新聚类结果。使用模拟和真实数据集呈现初始模拟，说明所提出算法的相关性。