Introducing Feature-Based Trajectory Clustering, a clustering algorithm for longitudinal data

TL;DR

This paper introduces a novel feature-based clustering algorithm for longitudinal data, which captures characteristic features of individuals' time-dependent variables and applies spectral clustering to identify meaningful groups.

Contribution

The paper proposes a new two-step clustering method that transforms longitudinal data into feature vectors and uses spectral clustering, improving the analysis of complex temporal patterns.

Findings

Effective identification of clusters with shared temporal features

Applicable to various types of longitudinal data

Demonstrates improved clustering accuracy

Abstract

We present a new algorithm for clustering longitudinal data. Data of this type can be conceptualized as consisting of individuals and, for each such individual, observations of a time-dependent variable made at various times. Generically, the specific way in which this variable evolves with time is different from one individual to the next. However, there may also be commonalities; specific characteristic features of the time evolution shared by many individuals. The purpose of the method we put forward is to find clusters of individual whose underlying time-dependent variables share such characteristic features. This is done in two steps. The first step identifies each individual to a point in Euclidean space whose coordinates are determined by specific mathematical formulae meant to capture a variety of characteristic features. The second step finds the clusters by applying the Spectral Clustering algorithm to the resulting point cloud.