Simultaneous clustering and variable selection: A novel algorithm and model selection procedure.

ABSTRACT: The growing availability of high-dimensional data sets offers behavioral scientists an unprecedented opportunity to integrate the information hidden in the novel types of data (e.g., genetic data, social media data, and GPS tracks, etc.,) and thereby obtain a more detailed and comprehensive view towards their research questions. In the context of clustering, analyzing the large volume of variables could potentially result in an accurate estimation or a novel discovery of underlying subgroups. However, a unique challenge is that the high-dimensional data sets likely involve a significant amount of irrelevant variables. These irrelevant variables do not contribute to the separation of clusters and they may mask cluster partitions. The current paper addresses this challenge by introducing a n

SUBMITTER: Yuan S 

PROVIDER: S-EPMC10439051 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature