首页 | 本学科首页   官方微博 | 高级检索  
     检索      


The Mixturegram: A Visualization Tool for Assessing the Number of Components in Finite Mixture Models
Authors:Derek S Young  Chenlu Ke  Xiaoxue Zeng
Institution:1. Department of Statistics, University of Kentucky, Lexington, KY;2. Apple Inc., Austin, TX
Abstract:Certain practical and theoretical challenges surround the estimation of finite mixture models. One such challenge is how to determine the number of components when this is not assumed a priori. Available methods in the literature are primarily numerical and lack any substantial visualization component. Traditional numerical methods include the calculation of information criteria and bootstrapping approaches; however, such methods have known technical issues regarding the necessary regularity conditions for testing the number of components. The ability to visualize an appropriate number of components for a finite mixture model could serve to supplement the results from traditional methods or provide visual evidence when results from such methods are inconclusive. Our research fills this gap through development of a visualization tool, which we call a mixturegram. This tool is easy to implement and provides a quick way for researchers to assess the number of components for their hypothesized mixture model. Mixtures of univariate or multivariate data can be assessed. We validate our visualization assessments by comparing with results from information criteria and an ad hoc selection criterion based on calculations used for the mixturegram. We also construct the mixturegram for two datasets.
Keywords:Cluster analysis  EM algorithm  Identifiability  Parallel coordinates  Principal components
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号