Abstract
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.
Highlights
Spectral clustering method has been proven successful in a number of problem domains, such as computer vision [1], speech recognition [2] and text extraction [3]
We look at the case of random arrival of data according to Stochastic Block Model (SBM)
METRICS We evaluate the quality of proposed scheme using the following metrics: Clustering Accuracy (CA) and Normalized
Summary
Spectral clustering method has been proven successful in a number of problem domains, such as computer vision [1], speech recognition [2] and text extraction [3]. We need to construct a similarity matrix and compute its corresponding spectrum It is computationally expensive when dealing with large data sets. This situation could be alleviated if we implement spectral clustering in a online way, which enables incrementally clustering of data without the need to run the clustering from scratch each time new data arrive. Incremental clustering methods are especially helpful when the data is essentially generated online, such as web-based resources like social networks. While it is important, incremental clustering is very difficult since it must update clusters dramatically as new data are added.
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