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论文类型：期刊论文

发表时间：2020-03-01

发表刊物：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录刊物：EI、SCIE

卷号：89

ISSN号：0952-1976

关键字：Data stream; Concept drift; Rough set; Clustering analysis; Neighborhood entropy

摘要：Clustering analysis is an important data mining method for data stream. In this paper, a self-adaption neighborhood density clustering method for mixed data stream is proposed. The method uses a significant metric criteria to make categorical attribute values become numeric and then the dimension of data is reduced by a nonlinear dimensionality reduction method. In the clustering method, each point is evaluated by neighborhood density. The k points are selected from the data set with maximum mutual distance after k is determined according to rough set. In addition, a new similarity measure based on neighborhood entropy is presented. The data points can be partitioned into the nearest cluster and the algorithm adaptively adjusts the clustering center points by clustering error. The experimental results show that the proposed method can obtain better clustering results than the comparison algorithms on the most data sets and the experimental results prove that the proposed algorithm is effective for data stream clustering.