Biclustering Models for Two-Mode Ordinal Data

Journal article

The work in this paper introduces finite mixture models that can be used to simul- taneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets

Authors: Matechou, E; Liu, I; Fernández, D; Farias, M; Gjelsvik, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cambridge University Press
• Journal: Psychometrika
• Volume: 81
• Issue: 3
• Pages: 611-624
• Publication date: 2016-06-23
• DOI: 10.1007/s11336-016-9503-3
• EISSN: 1860-0980
• ISSN: 0033-3123
• Language: English
• Keywords: Data mining; CURE data clustering algorithm; Cluster (spacecraft); Row and column spaces; Fuzzy clustering; Mathematics; Ordinal data; Biclustering; Computer science; Artificial intelligence; Row; Cluster analysis; Categorical variable; Likert scale; Ordinal Scale; Statistics; Fuzzy logic