A Survey on Evolutionary Co-Clustering Formulations for Mining Time-Varying Data Using Sparsity Learning

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Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
The data matrix is considered as static in Traditional clustering and feature selection methods. However, the data matrices evolve smoothly over time in many applications. A simple approach to learn from these time-evolving data matrices is to analyze them separately. Such strategy ignores the time-dependent nature of the underlying data. Two formulations are proposed for evolutionary co-clustering and feature selection based on the fused Lasso regularization. The evolutionary co-clustering formulation is able to identify smoothly varying data embedded into the matrices along with the temporal dimension.
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