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...
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 statistically better or comparable performance than stat...
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 ...
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....
A comparison of performance of automatic cloud coverage assessment algorithm for Formosat-2 image using clustering-based and spatial thresholding methods In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un...
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
In addition, a comparative analysis shows the advantage of clustering-based methods over supervised classification techniques in identifying new or unseen attack types. 展开 关键词: network intrusion detection clustering algorithms classification techniques ...
Density-based clustering methods do not typically require a priori knowledge of the number of clusters. Among these methods are the popular mean shift ([16], [17]) and DBSCAN ([6]) algorithms. These methods rely on the property that the density of points at the “core” of a cluster is...
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-...
fast algorithm now used by default ford= 1 (linear time complexity), a new option calledfastidiousthat refinesd= 1 results and reduces the number of small clusters. Common misconceptions swarmis a single-linkage clustering method, with some superficial similarities with other clustering methods (e....