An Approach to Deal With Time-Evolving Categorical Data Based on NIR Using Clustering

Data clustering is an important technique for exploratory data analysis and has been the focus of substantial research in several domains for decades among which Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points that are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain.

Provided by: Vivekananda Institute of Technology & Science Topic: Big Data Date Added: Sep 2011 Format: PDF

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