Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks
Wong Kam-Kwai Yan Luo Xuanwu Yue Wei Chen Huamin Qu
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.