Constrained k‐means on cluster proportion and distances among clusters for longitudinal data analysis
Constrained k‐means on cluster proportion and distances among clusters for longitudinal data analysis
复制标题
用于纵向数据分析的聚类比例和聚类间距离的约束 k 均值
DOI：
10.1111/jpr.12060
复制
发表时间：
2014
影响因子：
0.8
通讯作者：
S. Usami
中科院分区：
文献类型：
--
作者：
S. Usami
通过纵向数据变化轨迹上的相似性或相异性来对个体进行聚类，可以确定发育和生长的典型模式。本研究提出了一种新的约束k-means方法与下界约束的集群比例和集群之间的距离在重点变量和时间点，以满足各种需要的纵向数据聚类。该方法假设在开始时有大量的聚类，并根据这些约束迭代地删除和组合聚类。所提出的约束k-means的一个附加属性包括直接估计未知数量的聚类。仿真结果清楚地表明，该方法的有用性提取聚类在合理的，现实生活中的分析，包括非正态性内的集群，所提出的算法工作良好，收敛性的估计是令人满意的。一个实际的例子，使用日本的纵向数据，关于睡眠习惯和心理健康，以验证所提出的约束k-均值的效用。
Clustering individuals by measures of similarity or dissimilarity at trajectories of changes in longitudinal data enables determination of typical patterns of development and growth. The present research proposes a new constrained k-means method with lower bound constraints on cluster proportions and distances among clusters at focused variables and time points to fulfill various needs in clustering longitudinal data. The method assumes a large number of clusters at the onset and iteratively deletes and combines clusters according to these constraints. An additional property of the proposed constrained k-means includes direct estimation of the unknown number of clusters. Simulation results clearly show the usefulness of the method for extracting clusters in plausible, real-life analysis including non-normality within clusters, and the proposed algorithm works well and convergence of the estimates is satisfactory. An actual example using Japanese longitudinal data regarding sleep habits and mental health is presented to verify the utility of the proposed constrained k-means.
影响因子：
7
作者：
Vrieze, Scott I.
通讯作者：
Vrieze, Scott I.
影响因子：
7
作者：
Bauer, DJ;Curran, PJ
通讯作者：
Curran, PJ
影响因子：
7
作者：
Bauer, DJ;Curran, PJ
通讯作者：
Curran, PJ