On the Spectral Clustering for Dynamic Data
作者： D. H. Peluffo-Ordóñez , J. C. Alvarado-Pérez , A. E. Castro-Ospina
DOI: 10.1007/978-3-319-18833-1_16
关键词:
摘要: Spectral clustering has shown to be a powerful technique for grouping and/or rank data as well proper alternative unlabeled problems. Particularly, it is suitable when dealing with pattern recognition problems involving highly hardly separable classes. Due its versatility, applicability and feasibility, this results appealing many applications. Nevertheless, conventional spectral approaches lack the ability process dynamic or time-varying data. Within framework, work presents an overview of techniques their extensions analysis.
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