An intrusion detection system (IDS) is an active research topic and is regarded as one of the
important applications of machine learning. An IDS is a classifier that predicts the class of …

We develop and analyze a method to reduce the size of a very large set of data points in a
high-dimensional Euclidean space R^d to a small set of weighted points such that the result …

Given a metric space, the (k, z)-clustering problem consists of finding k centers such that the
sum of the of distances raised to the power z of every point to its closest center is minimized …

The (k, z)-clustering problem consists of finding a set of k points called centers, such that the
sum of distances raised to the power of z of every data point to its closest center is …

Motivated by practical generalizations of the classic k-median and k-means objectives, such
as clustering with size constraints, fair clustering, and Wasserstein barycenter, we introduce …

We study the problem of constructing ε-coresets for the (k, z)-clustering problem in a
doubling metric M (X, d). An ε-coreset is a weighted subset S⊆ X with weight function w: S→ …

In fully dynamic clustering problems, a clustering of a given data set in a metric space must
be maintained while it is modified through insertions and deletions of individual points. In …

We consider the classic Euclidean k-median and k-means objective on data streams, where
the goal is to provide a (1+ε)-approximation to the optimal k-median or k-means solution …

The notion of L p sampling, and corresponding algorithms known as L p samplers, has
found a wide range of applications in the design of data stream algorithms and beyond. In …

We consider classic clustering problems in fully dynamic data streams, where data elements
can be both inserted and deleted. In this context, several parameters are of importance:(1) …