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DWDP-Stream: A Dynamic Weight and Density Peaks Clustering Algorithm for Data Stream. INT J COMPUT INT SYS 2022. [DOI: 10.1007/s44196-022-00157-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
AbstractIdentifying clusters of arbitrary shapes and constantly processing the newly arrived data points are two critical challenges in the study of clustering. This paper proposes a dynamic weight and density peaks clustering algorithm to simultaneously solve these two key issues. An online–offline framework is used, creating and maintaining micro-clusters in the online phase, and treating the micro-clusters as pseudo-points to form the final cluster in the offline phase. In the online phase, when a new data point is merged into the corresponding micro-cluster, a dynamic weight method is proposed to update the weight of the micro-cluster according to the distance between the point and the center of the micro-cluster, so as to more accurately describe the information of the micro-cluster. In the offline phase, the density peak clustering algorithm is improved, natural neighbors are introduced to adaptively obtain the local density of the data point, and the allocation process is improved to reduce the probability of allocation errors. The algorithm is evaluated on different synthetic and real-world datasets using different quality metrics. The experimental results show that the proposed algorithm improves the clustering quality in both static and streaming environments.
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Chen J, Yang S, Wang Z. Multi-view representation learning for data stream clustering. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.09.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Jiao L, Yang H, Liu ZG, Pan Q. Interpretable fuzzy clustering using unsupervised fuzzy decision trees. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.08.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Otero A, Félix P, Márquez DG, García CA, Caffarena G. A fault-tolerant clustering algorithm for processing data from multiple streams. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.10.049] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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