Clustering Evolutionary Data with an r-Dominance Based Multi-objective Evolutionary Algorithm

Gao, Wenhao; Luo, Wenjian; Bu, Chenyang; Li, Ni; Zhang, Daofu

doi:10.1007/978-3-319-46257-8_37

Cited by 3 publications

(1 citation statement)

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“…This quantification may result in error propagation, i.e., the error in historical cluster centers/partition may mislead the current clustering. Additionally, almost all the existing methods [24] consider the temporal smoothness as an a priori preference, i.e., inserting smoothness into the clustering process and restricting the search of solutions along with the "preferred" direction. This a priori manner leads to a risk of losing solution diversity and converging to an unexpected region since it is not always the case that a reasonable preference can be elicited given the little prior knowledge about the data [19].…”

Section: A Temporal Data Clusteringmentioning

confidence: 99%

Evolutionary Robust Clustering Over Time for Temporal Data

Zhao,

Yan,

Shi

2021

Preprint

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In many clustering scenes, data samples' attribute values change over time. For such data, we are often interested in obtaining a partition for each time step and tracking the dynamic change of partitions. Normally, a smooth change is assumed for data to have a temporal smooth nature. Existing algorithms consider the temporal smoothness as an a priori preference and bias the search towards the preferred direction. This a priori manner leads to a risk of converging to an unexpected region because it is not always the case that a reasonable preference can be elicited given the little prior knowledge about the data. To address this issue, this paper proposes a new clustering framework called evolutionary robust clustering over time. One significant innovation of the proposed framework is processing the temporal smoothness in an a posteriori manner, which avoids unexpected convergence that occurs in existing algorithms. Furthermore, the proposed framework automatically tunes the weight of smoothness without data's affinity matrix and predefined parameters, which holds better applicability and scalability. The effectiveness and efficiency of the proposed framework are confirmed by comparing with state-of-the-art algorithms on both synthetic and real datasets.

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Section: A Temporal Data Clusteringmentioning

confidence: 99%