Graphint: Graph-based Time Series Clustering Visualisation Tool
This work addresses the need for effective analysis tools in diverse domains dealing with time series data, but it appears incremental as it builds on existing $k$-Graph methodology.
The paper tackles the problem of analyzing time series data by introducing Graphint, a system that integrates a clustering algorithm with an interactive visualization tool to maintain data relationships and improve interpretability, resulting in a comprehensive solution for extracting insights from temporal datasets.
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.