# Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks

arXiv: https://arxiv.org/abs/2402.08978 · HTML: https://mlsanity.com/paper/2402.08978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 81 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation](https://mlsanity.com/paper/2307.09036/) (2023)
- [Clustrophile 2: Guided Visual Clustering Analysis](https://mlsanity.com/paper/1804.03048/) (2018)
- [XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis](https://mlsanity.com/paper/2301.10385/) (2023)
- [Explaining with Examples: Lessons Learned from Crowdsourced Introductory Description of Information Visualizations](https://mlsanity.com/paper/2112.12364/) (2021)
- [Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events](https://mlsanity.com/paper/2302.06806/) (2023)
- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [Integrating Prior Knowledge in Mixed Initiative Social Network Clustering](https://mlsanity.com/paper/2005.02972/) (2020)
- [TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Group](https://mlsanity.com/paper/2009.03179/) (2020)
- [CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data](https://mlsanity.com/paper/2208.09237/) (2022)
- [The State of the Art in Multilayer Network Visualization](https://mlsanity.com/paper/1902.06815/) (2019)
- [ScrollTimes: Tracing the Provenance of Paintings as a Window into History](https://mlsanity.com/paper/2306.08834/) (2023)
- [sPortfolio: Stratified Visual Analysis of Stock Portfolios](https://mlsanity.com/paper/1910.05536/) (2019)
- [Geono-Cluster: Interactive Visual Cluster Analysis for Biologists](https://mlsanity.com/paper/1911.00988/) (2019)

## Abstract (arXiv)

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.