Enhancing the selection of a model-based clustering with external categorical variables

Enhancing the selection of a model-based clustering with external qualitative variables

In cluster analysis, it is often useful to interpret the obtained partition with respect to external qualitative variables (defining known partitions) derived from alternative information. An approach is proposed in the model-based clustering context to select a model and a number of clusters in order to get a partition which both provides a good fit with the data and is related to the external...

Model Selection Criteria for Model-Based Clustering of Categorical Time Series Data: A Monte Carlo Study

Model-based multidimensional clustering of categorical data

Existing models for cluster analysis typically consist of a number of attributes that describe the objects to be partitioned and one single latent variable that represents the clusters to be identified. When one analyzes data using such a model, one is looking for one way to cluster data that is jointly defined by all the attributes. In other words, one performs unidimensional clustering. This ...

investigating the feasibility of a proposed model for geometric design of deployable arch structures

deployable scissor type structures are composed of the so-called scissor-like elements (sles), which are connected to each other at an intermediate point through a pivotal connection and allow them to be folded into a compact bundle for storage or transport. several sles are connected to each other in order to form units with regular polygonal plan views. the sides and radii of the polygons are...

New Filter method for categorical variables’ selection

It is worth noting that the variable-selection process has become an increasingly exciting challenge, given the dramatic increase in the size of databases and the number of variables to be explored and modelized. Therefore, several strategies and methods have been developed with the aim of selecting the minimum number of variables while preserving as much information for the interest variable o...