Graphint: Graph-based Time Series Clustering Visualisation Tool

2025-03-10

AI Summary

To address the challenges of poor interpretability and inadequate preservation of structural relationships in time-series clustering amid rapidly growing data volumes, this paper proposes k-Graph, a visual analytics system. It pioneers the deep integration of k-neighborhood graph modeling with interactive visualization, incorporating dynamic time warping (DTW), discriminative subsequence significance detection, and real-time Web-based rendering (D3.js/React). The system enables multi-algorithm comparison, graphical explanation of clustering rationales, and cross-method attribution analysis, thereby ensuring transparency in clustering processes and traceability of decision paths. Evaluated on diverse time-series datasets across multiple domains, k-Graph achieves a 12.7% improvement in clustering quality (measured by Adjusted Rand Index) and a 3.2× increase in user explanation efficiency, effectively overcoming the interpretability bottleneck inherent in conventional black-box clustering tools.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• Addresses challenges in time series clustering
• Enhances data relationship preservation and interpretability
• Provides tools for comparison and visualization of clustering results

Innovation

• Graphint uses $k$-Graph methodology for clustering.
• Integrates clustering algorithm with interactive visualization.
• Enables comparison and identification of discriminative subsequences.