Fair $k$-Center Problem with Outliers on Massive Data

The clustering problem of big data in the era artificial intelligence has been widely studied. Because huge amount data, distributed algorithms are often used to deal with problems. computing model an attractive feature: it can handle massive datasets that cannot be put into main memory. On other hand, since many decisions made automatically by machines today's society, algorithm fairness is also important research area machine learning. In this paper, we study two fair problems: centralized $k$ -center outliers and outliers. For these problems, have designed corresponding constant approximation ratio algorithms. theoretical proof analysis ratio, running space given.