文章基本信息

标题：A Survey on Evolutionary Co-Clustering Formulations for Mining Time-Varying Data Using Sparsity Learning
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作者：R.Amsaveni ; R. Suresh Kumar
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：8
DOI：10.15680/IJIRCCE.2015. 0308006
出版社：S&S Publications
摘要：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-evolvingdata 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 Lassoregularization. The evolutionary co-clustering formulation is able to identify smoothly varying data embedded into thematrices along with the temporal dimension. Formulation allows for imposing smoothness constraints over temporaldimension of the data matrices. The evolutionary feature selection formulation can uncover shared features inclustering from time-evolving data matrices.
关键词：Time-varying data; Sparsity learning; co-clustering; feature selection; temporal smoothness.