Fuzzy Cluster-Aware Contrastive Clustering for Time Series
This addresses the challenge of uncovering patterns in unlabeled IoT time series data, though it appears incremental as it builds on existing deep learning-based clustering approaches.
The paper tackles the problem of unsupervised clustering for time series data by proposing a fuzzy cluster-aware contrastive clustering framework (FCACC) that jointly optimizes representation learning and clustering, achieving superior performance over eight baseline methods on 40 benchmark datasets.
The rapid growth of unlabeled time series data, driven by the Internet of Things (IoT), poses significant challenges in uncovering underlying patterns. Traditional unsupervised clustering methods often fail to capture the complex nature of time series data. Recent deep learning-based clustering approaches, while effective, struggle with insufficient representation learning and the integration of clustering objectives. To address these issues, we propose a fuzzy cluster-aware contrastive clustering framework (FCACC) that jointly optimizes representation learning and clustering. Our approach introduces a novel three-view data augmentation strategy to enhance feature extraction by leveraging various characteristics of time series data. Additionally, we propose a cluster-aware hard negative sample generation mechanism that dynamically constructs high-quality negative samples using clustering structure information, thereby improving the model's discriminative ability. By leveraging fuzzy clustering, FCACC dynamically generates cluster structures to guide the contrastive learning process, resulting in more accurate clustering. Extensive experiments on 40 benchmark datasets show that FCACC outperforms the selected baseline methods (eight in total), providing an effective solution for unsupervised time series learning.