We derive a new Bayesian Information Criterion (BIC) by formulating the problem of
estimating the number of clusters in an observed dataset as maximization of the posterior …

The gravitational clustering algorithm is a dynamic clustering model that achieves
outstanding performance in uncovering the hidden clusters of a complex dataset with any …

Bayesianoptimization (BO) provides an efficient tool for solving the black-box global
optimization problems. Under situations where multiple points can be evaluated …

Robustly determining the optimal number of clusters in a data set is an essential factor in a
wide range of applications. Cluster enumeration becomes challenging when the true …

Accurately clustering large, high dimensional datasets is a challenging problem in
unsupervised learning. K-means is considered to be a fast, widely used and accurate …

IoT (Internet-of-Things) devices require both reliable, ultra-low latency connection and on-
demand access to computing resources in their vicinity. Edge computing can provide nearby …

A major challenge in cluster analysis is that the number of data clusters is mostly unknown
and it must be estimated prior to clustering the observed data. In real-world applications, the …

Multiagent distributed clustering scheme is proposed herein to process data which are
collected by dispersed sensors that are not under centralized control. Two methods based …

Distributed signal processing for sensor networks with node-specific interest requires the
common labeling of all objects of interest. Current methods formulate the labeling task as a …

Emerging applications with low-latency requirements such as real-time analytics, immersive
media applications, and intelligent virtual assistants have rendered Edge Computing as a …