Clustering Time Series Data Considering Both Trend Filtering and Subject Level Closeness
Zhiren Wang · 2025
This paper presents a novel clustering methodology that integrates both longitudinal trends and multi-dimensional proximity of lung cancer rates across 50 U.S. states and Washington D.C. from 1969 to 2019. By synthesizing temporal dynamics and local similarities, the proposed hybrid algorithm refine