In this paper we review studies of the growth of the Internet and technologies that are useful
for information search and retrieval on the Web. We present data on the Internet from several …

In this work, we explore the unique challenges—and opportunities—of unsupervised
federated learning (FL). We develop and analyze a one-shot federated clustering scheme …

Enterprises today are dealing with the massive size of data, which have been explosively
increasing. The key requirements to address this challenge are to extract, analyze, and …

This paper is concerned with developing a distributed k-means algorithm and a distributed
fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped …

Over half a century old and showing no signs of aging, k-means remains one of the most
popular data processing algorithms. As is well-known, a proper initialization of k-means is …

Organizing data into sensible groupings is one of the most fundamental modes of
understanding and learning. As an example, a common scheme of scientific classification …

Clustering is the division of data into groups of similar objects. In clustering, some details are
disregarded in exchange for data simplification. Clustering can be viewed as a data …

With the ever-increasing diffusion of smart devices and Internet of Things (IoT) applications,
a completely new set of challenges have been added to the Data Mining domain. Edge …

Unlabeled document collections are becoming increasingly common and available; mining
such data sets represents a major contemporary challenge. Using words as features, text …

Spectral clustering algorithms have been shown to be more effective in finding clusters than
some traditional algorithms, such as k-means. However, spectral clustering suffers from a …