Evolutionary Robust Clustering Over Time for Temporal Data

Qi Zhao

doi:10.1109/TCYB.2022.3167711

Evolutionary Robust Clustering Over Time for Temporal Data

2023-07-01

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AI

DOI：10.1109/TCYB.2022.3167711

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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 toward 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 article 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 infers the a posteriori preference to temporal smoothness without data's affinity matrix and predefined parameters, which holds better applicability and efficiency. The effectiveness and efficiency of the proposed framework are confirmed by comparing with state-of-the-art algorithms on both synthetic and real datasets.

Keywords:

Clustering algorithms

Partitioning algorithms

Optimization

Indexes

Linear programming

Tuning

Task analysis

Dynamic optimization

evolutionary algorithm (EA)

evolutionary clustering

temporal data

Evolutionary Robust Clustering Over Time for Temporal Data

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