Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks
<|reference_start|>Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks: Financial cluster analysis allows investors to discover investment alternatives and avoid undertaking excessive risks. However, this analytical task faces substantial challenges arising from many pairwise comparisons, the dynamic correlations across time spans, and the ambiguity in deriving implications from business relational knowledge. We propose Prismatic, a visual analytics system that integrates quantitative analysis of historical performance and qualitative analysis of business relational knowledge to cluster correlated businesses interactively. Prismatic features three clustering processes: dynamic cluster generation, knowledge-based cluster exploration, and correlation-based cluster validation. Utilizing a multi-view clustering approach, it enriches data-driven clusters with knowledge-driven similarity, providing a nuanced understanding of business correlations. Through well-coordinated visual views, Prismatic facilitates a comprehensive interpretation of intertwined quantitative and qualitative features, demonstrating its usefulness and effectiveness via case studies on formulating concept stocks and extensive interviews with domain experts.<|reference_end|>
@article{kam-kwai2024prismatic:,
    title={Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks},
    author={Wong Kam-Kwai, Yan Luo, Xuanwu Yue, Wei Chen, Huamin Qu},
    journal={arXiv preprint arXiv:2402.08978},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08978},
    primaryClass={cs.HC cs.CE cs.LG}
}