CMQ uses a technique called vector quantization, which clusters inter-partition nodes and represents them using points called centroids.

Our approach begins by decomposing the problem of finding k clusters into hierarchical clustering, where each level of the hierarchy consists of binary clustering. We then introduce a method to ...

This paper applies the K-means and Fuzzy C-means clustering algorithms to a vehicle crash dataset in order to explore various patterns in the data. K-means assigns data points to clusters based on the ...

This paper applies the K-means and Fuzzy C-means clustering algorithms to a vehicle crash dataset in order to explore various patterns in the data. K-means assigns data points to clusters based on the ...

A Cluster-Assisted Differential Evolution-Based Hybrid Oversampling Method for Imbalanced Datasets 📌 ClusterDEBO: A Cluster-Assisted Differential Evolution-Based Hybrid Oversampling Algorithm This ...

(b) Comparison of cluster centroids between datasets, with results matched column-wise. Bright labels correspond to the 3T and 3T retest datasets. (c) Dice overlap scores and centroid distances for ...

BuildNicheAssay on multiple samples, get niches named the same across samples, and filtering cells before building niches #9509 ...