Clustering Categorical data - Devarakonda, Prashanth Kumar;Nangunuri, Raghu
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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.

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Produktbeschreibung
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.
Autorenporträt
Prashanth Kumar Devarakonda completed both Masters and Bachelors program in Computer Science and Engineering in 2010 and 2005 respectively. Keen interests are in the field of Data Mining and has Eight years of work experience in that area.