Spatial data miningGISFriuli Venezia Giulia RegionItalyIn this paper we present the finite mixture models approach to clustering of high dimensional data. The mixture resolving approach to cluster analysis has
degree in Applied Statistics from Bowling Green State University. He is currently a Professor at the University of Alabama. He also serves on the Board of Directors of Classification Society of North America. His main research interests include model based clustering methods, clustering high-...
In computational homogenization approaches, data-driven methods entail advantages due to their ability to capture complex behavior without assuming a specific material model. Within this domain, constitutive model-based and model-free data-driven methods are distinguished. The former employ artificial neural...
There are several methods in the literature [22], [23], [24], [24], [25], which have ignored the feature redundancy issue in the selection process. To overcome the said issue, the current approach has used attribute clustering based feature selection technique. In the approach the ...
Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two classes of methods prevail: machine learning models built on
The data mining method in the education sector consists of statistics, concept visualization, applying classification, creating clustering with associative based analysis, applying anomaly identification, and involves text-based mining [4]. A previous study showed that [5] the education sector applies ...
This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in patients with ischemic stroke. Prospective stroke registry data from a comprehensive stroke center from January ...
Clustering, or the unsupervised classification of data items into clusters, can reveal some intrinsic structures among data. The intrinsic structures, like the number of clusters, are key issues of data mining. In this paper, we propose some genetic-guided model-based c...
Model-based clustering is a popular technique relying on the notion of finite mixture models that proved to be efficient in modeling heterogeneity in data. The underlying idea is to model each data group by a particular mixture component. This relationship between mixed distributions and clusters for...
(2005), that a methodology based on a combination of clustering and classification tools (a fuzzy system) for leak detection has been presented. In most industrial processes data collection with the most accuracy has its particular importance. It has been approved that consuming multi-sensor arrays...