  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.
