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k-means is a benchmark algorithm used in cluster analysis. It belongs to the large category of heuristics based on location-allocation steps that alternately locate cluster centers and allocate data points to them until no further improvement is possible. Such heuristics are known to suffer from...
Ultrametric inequality is involved in different operations on (dis)similarity matrices. Its coupling with a compatible ordering leads to nice interpretations in seriation problems. We accurately review the interval graph recognition problem for its tight connection with recognizing a dense...
Multiple criteria have been proposed to aid in deciding how many latent classes to extract in growth mixture models; however, studies are just beginning to investigate the performance of these criteria under non-ideal conditions. We review these previous studies and conduct a simulation study to...
The Gaussian cluster-weighted model (CWM) is a mixture of regression models with random covariates that allows for flexible clustering of a random vector composed of response variables and covariates. In each mixture component, a Gaussian distribution is adopted for both the covariates and the...
We propose a new method for finding an optimal cluster structure of functions as well as an optimal subspace for clustering simultaneously. The proposed method aims to minimize a distance between functional objects and their projections with the imposition of clustering penalties. It includes...
A critical issue in analyzing multi-item scales is missing data treatment. Previous studies on this topic in the framework of item response theory have shown that imputation procedures are in general associated with more accurate estimates of item location and discrimination parameters under...
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