69 research outputs found
Benchmarking in cluster analysis: A white paper
To achieve scientific progress in terms of building a cumulative body of
knowledge, careful attention to benchmarking is of the utmost importance. This
means that proposals of new methods of data pre-processing, new data-analytic
techniques, and new methods of output post-processing, should be extensively
and carefully compared with existing alternatives, and that existing methods
should be subjected to neutral comparison studies. To date, benchmarking and
recommendations for benchmarking have been frequently seen in the context of
supervised learning. Unfortunately, there has been a dearth of guidelines for
benchmarking in an unsupervised setting, with the area of clustering as an
important subdomain. To address this problem, discussion is given to the
theoretical conceptual underpinnings of benchmarking in the field of cluster
analysis by means of simulated as well as empirical data. Subsequently, the
practicalities of how to address benchmarking questions in clustering are dealt
with, and foundational recommendations are made
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
A Tabu-Search Heuristic for Deterministic Two-Mode Blockmodeling of Binary Network Matrices
clustering, two-mode networks, blockmodeling, tabu search, heuristics,
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