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Found 9 papers, 3 papers with code
Optimized neural forms for solving ordinary differential equations
A critical issue in approximating solutions of ordinary differential equations using neural networks is the exact satisfaction of the boundary or initial conditions.
Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters
Soft silhouette rewards compact and distinctly separated clustering solutions like the conventional silhouette coefficient.
Silhouette Aggregation: From Micro to Macro
Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment.
UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters
In this work, we focus on the concept of unimodality and propose a flexible cluster definition called locally unimodal cluster.
A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections
Unimodality, pivotal in statistical analysis, offers insights into dataset structures and drives sophisticated analytical procedures.
Global $k$-means$++$: an effective relaxation of the global $k$-means clustering algorithm
The global $k$-means is a deterministic algorithm proposed to tackle the random initialization problem of k-means but its well-known that requires high computational cost.
The UU-test for Statistical Modeling of Unimodal Data
We propose a technique called UU-test (Unimodal Uniform test) to decide on the unimodality of a one-dimensional dataset.
A Replication Strategy for Mobile Opportunistic Networks based on Utility Clustering
Dynamic replication is a wide-spread multi-copy routing approach for efficiently coping with the intermittent connectivity in mobile opportunistic networks.
Dip-means: an incremental clustering method for estimating the number of clusters
The proposed algorithm considers each cluster member as a ''viewer'' and applies a univariate statistic hypothesis test for unimodality (dip-test) on the distribution of the distances between the viewer and the cluster members.
