A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Mixture model clustering for mixed data with missing information
Computational Statistics & Data Analysis
A Comparison Study of Cluster Validity Indices Using a Nonhierarchical Clustering Algorithm
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Crisp and fuzzy k-means clustering algorithms for multivariate functional data
Computational Statistics
Cluster Analysis
Short communication: Optimising k-means clustering results with standard software packages
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
KmL3D: A non-parametric algorithm for clustering joint trajectories
Computer Methods and Programs in Biomedicine
mmm: An R package for analyzing multivariate longitudinal data with multivariate marginal models
Computer Methods and Programs in Biomedicine
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Cohort studies are becoming essential tools in epidemiological research. In these studies, measurements are not restricted to single variables but can be seen as trajectories. Thus, an important question concerns the existence of homogeneous patient trajectories. KmL is an R package providing an implementation of k-means designed to work specifically on longitudinal data. It provides several different techniques for dealing with missing values in trajectories (classical ones like linear interpolation or LOCF but also new ones like copyMean). It can run k-means with distances specifically designed for longitudinal data (like Frechet distance or any user-defined distance). Its graphical interface helps the user to choose the appropriate number of clusters when classic criteria are not efficient. It also provides an easy way to export graphical representations of the mean trajectories resulting from the clustering. Finally, it runs the algorithm several times, using various kinds of starting conditions and/or numbers of clusters to be sought, thus sparing the user a lot of manual re-sampling.