Mining Evolutionary Events from Multi-Streams Based on Spectral Clustering
基于谱聚类的多数据流演化事件挖掘

To solve the problem of mining evolutionary events from multi-streams, this paper proposes a spectral clustering algorithm, SCAM (spectral clustering algorithm of multi-streams), to generate the clustering models of Multi-Streams. The similarity matrix in the clustering models of Multi-Streams are based on Coupling Degree, which measures the dynamic similarity between two streams. In addition, this paper also proposes an algorithm, EEMA (evolutionary events mining algorithm), to discover the evolutionary event points based on the drift of clustering models. EEMA takes the index of Clustering Model Quality as the optimization objective in determing the number of clusters automatically. The Clustering Model Quality combines the matrix perturbation theory and the Clustering Cohesion, which has a sound upper bound and is used to measure the compactness of a clustering model. Finally, this paper presents O-EEMA (optimized-EEMA) as the optimization of EEMA with the temporal complexity of O(cn2/2), and the results of extensive experiments on the synthetic and real data set show that EEMA and O-EEMA are effective and practicable.