With the exponential growth of time series data across diverse domains, there is a pressing need for effective analysis tools. Time series clustering is important for identifying patterns in these datasets. However, prevailing methods often encounter obstacles in maintaining data relationships and ensuring interpretability. We present Graphint, an innovative system based on the$k$-Graph methodology that addresses these challenges. Graphint integrates a robust time series clustering algorithm with an interactive tool for comparison and interpretation. More precisely, our system allows users to compare results against competing approaches, identify discriminative subsequences within specified datasets, and visualize the critical information utilized by$k$-Graph to generate outputs. Overall, Graphint offers a comprehensive solution for extracting actionable insights from complex temporal datasets.

Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with centroids. In addressing this gap, our work presents$k$-Graph, an unsupervised method explicitly crafted to augment interpretability in time series clustering. Leveraging a graph representation of time series subsequences,$k$-Graph constructs multiple graph representations based on different subsequence lengths. This feature accommodates variable-length time series without requiring users to predetermine subsequence lengths. Our experimental results reveal that$k$-Graph outperforms current state-of-the-art time series clustering algorithms in accuracy, while providing users with meaningful explanations and interpretations of the clustering outcomes.

Deep learning models achieve state-of-the-art per-formance in solving the task of Entity Matching, which aims to identify records that refer to the same real-world entity. However, they act as black-box models for the user, who has limited insights into the rationales behind their decisions. Several explainers (e.g., LIME, Mojito, Landmark, LEMON, and CERTA) have been proposed in the literature to address this issue. Their main focus is to generate explanations that are faithful to the model without considering their comprehensibility to the user. For example, verbose explanations could be very complex to analyze, hindering the model's understanding. In this paper, we propose CREW, an explanation system for Entity Matching models that combines the comprehensibility of the explanations and fidelity to the model. To achieve this, CREW creates explanations as clusters of words. The clusters are created by exploiting three different forms of knowledge: the semantic similarity of the words, their arrangement into the dataset attributes, and their importance in explaining the model. Experiments show that CREW generates explanations that are more interpretable for the user and more faithful to the model than those generated by competing explanation techniques.

This paper showcases Time2Feat, an end-to-end machine learning system for Multivariate Time Series (MTS) clustering. The system relies on interpretable inter-signal and intra-signal features extracted from the time series. Then, a dimensionality reduction technique is applied to select a subset of features that retain most of the information, thus enhancing the interpretability of the results. In addition, the system enables domain specialists to semi-supervise the process by submitting a small collection of MTS with a target cluster. This process further improves both accuracy and interpretability, by reducing the number of features used by the clustering process. The demonstration shows the application of Time2Feat to various MTS datasets, by creating clusters from MTS datasets of interest, experimenting with different settings and using the approach capabilities to interpret the clusters generated.

Clustering multivariate time series is a critical task in many real-world applications involving multiple signals and sensors. Existing systems aim to maximize effectiveness, efficiency and scalability, but fail to guarantee the interpretability of the results. This hinders their application in critical real scenarios where human comprehension of algorithmic behavior is required. This paper introduces Time2Feat, an end-to-end machine learning system for multivariate time series (MTS) clustering. The system relies on inter-signal and intra-signal interpretable features extracted from the time series. Then, a dimensionality reduction technique is applied to select a subset of features that retain most of the information, thus enhancing the interpretability of the results. In addition, domain experts can semi-supervise the process, by providing a small amount of MTS with a target cluster. This process further improves both accuracy and interpretability, narrowing down the number of features used by the clustering process. We demonstrate the effectiveness, interpretability, efficiency, and robustness of Time2Feat through experiments on eighteen benchmarking time series datasets, comparing them with state-of-the-art MTS clustering methods.

International audience

Clustering time series is a recurrent problem in real-life applications involving data science and data analytics pipelines. Existing time series clustering algorithms are ineffective for feature-rich real-world time series since they only compare the time series based on raw data or use a fixed set of features for determining the similarity. In this paper, we showcase FeatTS, a feature-based semi-supervised clustering framework addressing the above issues for variable-length and heterogeneous time series. Specifically, FeatTS leverages a graph encoding of the time series that is obtained by considering a high number of significant extracted features. It then employs community detection and builds upon a Co-Occurrence matrix in order to unify all the best clustering results. We let the user explore the various steps of FeatTS by visualizing the initial data, its graph encoding and its division into communities along with the obtained clusters. We show how the user can interact with the process for the choice of the features and for varying the percentage of input labels and the various parameters. In view of its characteristics, FeatTS outperforms the state of the art clustering methods and is the first to be able to digest domain-specific time series such as healthcare time series, while still being robust and scalable.