This paper considers k-means clustering in the presence of noise. It is known that k-means
clustering is highly sensitive to noise, and thus noise should be removed to obtain a quality …

Multi-view or even multi-modal data is appealing yet challenging for real-world applications.
Detecting anomalies in multi-view data is a prominent recent research topic. However, most …

In this paper, we study the $ k $-clustering problems with outliers in distributed setting. The
current best results for the distributed $ k $-center problem with outliers have quadratic local …

Greedy algorithms such as adaptive sampling (k-means++) and furthest point traversal are
popular choices for clustering problems. One the one hand, they possess good theoretical …

In this paper, we consider the $ k $-center/median/means clustering with outliers problems
(or the $(k, z) $-center/median/means problems) in the distributed setting. Most previous …

Recent demands on data privacy have called for federated learning (FL) as a new
distributed learning paradigm in massive and heterogeneous networks. Although many FL …

This paper presents a batch-wise density-based clustering approach for local outlier
detection in massive-scale datasets. Unlike the well-known traditional algorithms, which …

Outlierdetection is an important research direction in data mining, including fraud detection,
activity monitoring, medical research, network intrusion detection, etc. Many outlier detection …

Clustering is a pivotal primitive for unsupervised learning and data analysis. A popular
variant is the (k, ℓ)-clustering problem, where, given a pointset P from a metric space, one …

Federated learning (FL) is encountered with the challenge of training a model in massive
and heterogeneous networks. Model averaging (MA) has become a popular FL paradigm …