Prismatic：概念股的交互式多视图聚类分析
Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks
AI总结：
Prismatic是一个视觉分析系统，通过整合历史绩效定量分析与业务关系知识定性分析，采用多视图聚类方法交互式聚类相关业务，帮助投资者发现投资替代方案并规避过度风险。
摘要：
金融聚类分析使投资者能够发现投资替代方案并避免承担过度风险。然而，由于需要进行大量成对比较、不同时间跨度上的动态相关性，以及从业务关系知识中推导含义的模糊性，这一分析任务面临重大挑战。我们提出了Prismatic，一个视觉分析系统，它整合了对历史绩效的定量分析和业务关系知识的定性分析，以交互方式聚类相关业务。Prismatic具有三个聚类过程：动态聚类生成、基于知识的聚类探索和基于相关性的聚类验证。利用多视图聚类方法，它通过知识驱动的相似性丰富了数据驱动的聚类，提供了对业务相关性的细致理解。通过协调良好的视觉视图，Prismatic促进了对交织的定量和定性特征的全面解释，通过制定概念股的案例研究和与领域专家的广泛访谈，展示了其实用性和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
