Parallel Coordinates Graph in Bundling Technique

Parallel coordinates are well-known instruments for large and multi-dimensional data set visualization. However, data cluttering and overplotting are significant problems for graphing parallel coordinates and becoming worst in the Big Data age. Thus, by bundling comparable edges or polylines in parallel coordinate graph, the bundling method has overcome the above mentioned issues. This research utilizes one of the bundling clustering techniques applied to a parallel coordinate graph to show and explore the information relationship pattern. A clustering prototype called PCG In Bundling is being developed with the aim of visually interpreting the data by converting the static data form into an interactive form. The photovoltaic dataset used in this project was primarily gathered from the UiTM Shah Alam Green Energy Research Centre. Currently, the dataset is stored in Microsoft Excel, which is static and hardly interpretable. This project therefore seeks to assist users analyze and discover the relationship patterns on the dataset by visualizing the graph of parallel coordinates. Bundling method has therefore been introduced to the parallel coordinate graph of photovoltaic datasets to cluster information and help users discover data relationship patterns. The functionality of this clustering prototype has been evaluated. As anticipated, the cost-based edge-bundling algorithm implemented in this project is efficient and can assist users analyze and interpret data relationship patterns. To conclude, this project is a success and fulfilled all the necessary scopes.