@inproceedings{mapperinteractive_chalapathi_2021,
  author={Zhou, Youjia and Chalapathi, Nithin and Rathore, Archit and Zhao, Yaodong and Wang, Bei},
  booktitle={2021 IEEE 14th Pacific Visualization Symposium (PacificVis)}, 
  title={Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data}, 
  year={2021},
  volume={},
  number={},
  pages={101-110},
  keywords={Visualization;Data analysis;Three-dimensional displays;Data visualization;Tools;Data mining;Scalability Issues;High Dimensional Data;Topology based Techniques},
  doi={10.1109/PacificVis52677.2021.00021},
  pdf={mapperinteractive_chalapathi_2021.pdf},
  preview={mapperinteractive_chalapathi_2021_preview.png},
  abstract={The mapper algorithm is a popular tool from topological data analysis for extracting topological summaries of high-dimensional datasets. In this paper, we present Mapper Interactive, a web-based framework for the interactive analysis and visualization of high-dimensional point cloud data. It implements the mapper algorithm in an interactive, scalable, and easily extendable way, thus supporting practical data analysis. In particular, its command-line API can compute mapper graphs for 1 million points of 256 dimensions in about 3 minutes (4 times faster than the vanilla implementation). Its visual interface allows on-the-fly computation and manipulation of the mapper graph based on user-specified parameters and supports the addition of new analysis modules with a few lines of code. Mapper Interactive makes the mapper algorithm accessible to nonspecialists and accelerates topological analytics workflows.},
  code={https://github.com/MapperInteractive/MapperInteractive},
  website={https://mapperinteractive.github.io/},
  }