The user data can be clustered based on one or more latent behavioral functions. Likelihood parameters of a user preference can be determined based on the clustering, and a recommendation can be determined based on the likelihood parameters.LUMBRERAS AlbertoGUEGAN MarieVELCIN JulienJOUVE Bertrand...
across the boundary among adjacent clusters. Additionally, we developed an effective diversity exploration strategy to address the redundancy among queried samples. Our extensive experimentation provided a comparison of the ALCS approach with state-of-the-art methods, exhibiting that ALCS produces statistica...
It is also faster than similar clusterization methods that are sensitive to density and shapes such as Mitosis and TRICLUST. In addition, k-MS is deterministic and has an intrinsic sense of maximal clusters that can be created for a given input sample and input parameters, differing from k-...
Most of the existing clustering methods have difficulty in processing complex nonlinear data sets. To remedy this deficiency, in this paper, a novel data model termed Hybrid K-Nearest-Neighbor (HKNN) graph, which combines the advantages of mutual k-nearest-neighbor graph and k-nearest-neighbor ...
R. (1999), Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences, 96, 2907–2912. Google Scholar Tibshirani, R., Walther, G., Botstein, D., & Brown, P. (2001a). Cluster...
Methods and systems for session clustering based on user experience, behavior, and interactionsA server system sorts a plurality of sessions for multiple users of a media-providing service into a plurality of groups by applying one or more sorting rules to one or more session characteristics for ...
four methods based on the TF-IDF transformation, out of which we selected nineteen methods for inclusion in the final comparison. A summary of the compared methods is given in Fig.1. We next describe the common data processing employed for all methods, then give details of individual methods....
Clustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering me
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-dimensional objects, and data visualization....
The existing methods usually include the effective independence (EFI) [19–21], stochastic EFI [22], interval EFI [23,24], information entropy [25,26], intelligent optimization [27–31], and so on. However, all of the methods are developed for the model updating, structural health ...