This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data sources, three types of comparisons of resulting c...

This paper describes the application of Dis tributional Clustering to document classi cation This approach clusters words into groups based on the distribution of class labels associated with each word Thus unlike some other unsupervised dimensionality reduction techniques such as Latent Semantic Indexing we are able to compress the feature space much more aggressively while still maintaining h...

This article examines the effects of clustering in latent class analysis. A comprehensive simulation study is conducted, which begins by specifying a true multilevel latent class model with varying withinand between-cluster sample sizes, varying latent class proportions, and varying intraclass correlations. These models are then estimated under the assumption of a single-level latent class mode...

conclusions fib-4 confirmed the best value for diagnosis of significant fibrosis. apri had a sub-optimal diagnosis accuracy for significant fibrosis. lsm showed the most balance diagnosis value for cirrhosis with the highest sensitivity and moderate specificity. patients and methods a total of 544 patients with chb were assessed for fibrosis stage by four noninvasive tests containing liver stif...

Latent class models provide a useful framework for clustering observations based on several features. Application of latent class methodology to correlated, high-dimensional ordinal data poses many challenges. Unconstrained analyses may not result in an estimable model. Thus, information contained in ordinal variables may not be fully exploited by researchers. We develop a penalized latent clas...

Traffic accident data are often heterogeneous, which can cause certain relationships to remain hidden. Therefore, traffic accident analysis is often performed on a small subset of traffic accidents or several models are built for various traffic accident types. In this paper, we examine the effectiveness of a clustering technique, i.e. latent class clustering, for identifying homogenous traffic...

This paper describes and compares three clustering techniques: traditional methods, Kohonen maps latent class models. The also proposes some novel measures of the quality a clustering. To best our knowledge, this is first contribution in literature to compare these techniques context where classes are not known advance.