Tax risk behavior causes serious loss of fiscal revenue, damages the country's public
infrastructure, and disturbs the market economic order of fair competition. In recent years, tax …

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 …

As a representative model for privacy preserving data publishing, K-anonymity has raised a
considerable number of questions for researchers over the past few decades. Among them …

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 …

We study the problem of $ k $-center clustering with outliers in arbitrary metrics and
Euclidean space. Though a number of methods have been developed in the past decades, it …

The rapid development of the mobile Internet coupled with the widespread use of intelligent
terminals have intensified the digitization of personal information and accelerated the …

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 …

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 …

The clustering problem has been paid lots of attention in various fields of compute science.
However, in many applications, the existence of noisy data poses a big challenge for the …

We study the classic k-means/median clustering, which are fundamental problems in
unsupervised learning, in the setting where data are partitioned across multiple sites, and …