基于特征的轨迹聚类，可发现长期数据中共享相似演化模式的个体群组。
Introducing Feature-Based Trajectory Clustering, a clustering algorithm for longitudinal data
- 将每个个体的时间演化特征转化为欧氏空间中的点
- 通过谱聚类算法识别出具有相似特征的个体群组
- 适用于医学、行为科学等长期追踪数据研究
我们提出一种新的纵向数据聚类算法。这类数据可理解为多个个体，每个个体在不同时刻对一个随时间变化的变量进行观测。虽然不同个体的变量演化方式各不相同，但可能存在共同特征。本文方法旨在识别出那些时间演化特征相似的个体群组。该方法分为两步：第一步，利用数学公式将每个个体映射到欧氏空间中的一个点，其坐标捕捉多种特征；第二步，对生成的点云应用谱聚类算法进行聚类。
原文摘要 · Abstract (English)
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.
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