Analysis of Concept Drift Detection – A Framework for Categorical Time Evolving Data

Cluster sampling has been recognized as an important technique to improve the efficiency of clustering. Sampling applied, those points that are not sampled will not have their labels after the normal process. An 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. In this paper, a mechanism categorical clustering algorithm proposed to allocate each unlabeled data point into the corresponding appropriate cluster based on the novel categorical clustering representative. Our work studies the best algorithm by using data labeling and outlier detection that have not been used before. We analyses the algorithm that have the more efficiency or learning and describes the proposed system of categorical clustering time-evolving