Low-Rank Kernel Matrix Factorization for Large-Scale Evolutionary Clustering

—Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorithm to adapt to the recent changes in the evolving data, but it also needs to take the historical relationship between the data points into consideration. In this paper, we propose ECKF, a general framework for evolutionary clustering large-scale data based on low-rank kernel matrix factorization. To the best of our knowledge, this is the first work that clusters large evolutionary datasets by the amalgamation of low-rank matrix approximation methods and matrix factorization based clustering. Since the low-rank approximation provides a compact representation of the original matrix, and especially, the near-optimal low-rank approximation can preserve the sparsity of the original data, ECKF gains computational efficiency and hence is applicable to large evolutionary datasets. Moreover, matrix factorization base...

• Type: Journal
• Year: 2012
• Where: TKDE
• Authors: Lijun Wang, Manjeet Rege, Ming Dong, Yongsheng Ding
• Keywords: Approximation Methods, Formal Methods, Matrix Factorization, Theoretical Standpoint, TKDE 2012