Introducing Feature-Based Trajectory Clustering, a clustering algorithm for longitudinal data
This addresses the need for clustering methods in longitudinal data analysis, but it appears incremental as it combines existing feature extraction with spectral clustering.
The paper tackles the problem of clustering longitudinal data by identifying individuals with shared characteristic features in their time-dependent variables, resulting in a two-step algorithm that maps individuals to Euclidean space and applies spectral clustering.
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.