Clustering Time Series Data Considering Both Trend Filtering and Subject Level Closeness

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

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.4108/eai.21-11-2024.2354637
Primary Topic
Time Series Analysis and Forecasting
Type
article
Field-Weighted Citation Impact
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article

Clustering Time Series Data Considering Both Trend Filtering and Subject Level Closeness

Zhiren Wang
Time Series Analysis and Forecasting
article

Clustering Time Series Data Considering Both Trend Filtering and Subject Level Closeness

Zhiren Wang
article en

Abstract

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

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Clustering Time Series Data Considering Both Trend Filtering and Subject Level Closeness — Zhiren Wang · (2025) | TGRS Research Map | TGRS