Evolutionary Robust Clustering Over Time for Temporal Data

TL;DR

This paper introduces a new evolutionary clustering framework that adaptively ensures temporal smoothness in data partitioning over time without relying on prior assumptions, improving robustness and scalability.

Contribution

It proposes an a posteriori approach to temporal smoothness in clustering, avoiding convergence issues and eliminating the need for predefined parameters or affinity matrices.

Findings

Outperforms state-of-the-art algorithms on synthetic datasets

Demonstrates effectiveness on real-world temporal data

Automatically tunes smoothness weights without prior knowledge

Abstract

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.