The analysis of bridging constructs with hierarchical clustering methods: An application to identity

When analyzing psychometric surveys, some design and sample size limitations challenge existing approaches. Hierarchical clustering, with its graphics (heat maps, dendrograms, means plots), provides a nonparametric method for analyzing factorially-designed survey data, and small samples data. In the present study, we demonstrated the advantages of using hierarchical clustering (HC) for the analysis of non-higher-order measures, comparing the results of HC against those of exploratory factor analysis. As a factorially-designed survey, we used the Identity Labels and Life Contexts Questionnaire (ILLCQ), a novel measure to assess identity as a bridging construct for the intersection of identity domains and life contexts. Results suggest that, when used to validate factorially-designed measures, HC and its graphics are more stable and consistent compared to EFA

Walktrap Using Kmeans Clustering

This zip file contains the MATLAB files required to replicate the simullation results in the paper: Improving the Walktrap Algorithm Using K-means Clustering</p

MATLAB OCD Files

These MATLAB files can be used to produce the simulation results in the paper titled: A Mexima0-Clique-Based Set-Covering Approach to Overlapping Community Detection".</p

Order-Constrained Solutions in K-Means Clustering: Even Better Than Being Globally Optimal

K-means cluster analysis, dynamic programming, quadratic assignment, constrained optimization, multicriterion optimization,