Many clustering algorithms lead to cluster assignments that are hard to explain, partially
because they depend on all the features of the data in a complicated way. To improve …

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 …

K-means is one of the most simple and popular clustering algorithms, which implemented as
a standard clustering method in most of machine learning researches. The goal of K-means …

The estimation of remaining discharge energy (RDE) of lithium-ion batteries is the basis for
the remaining driving range estimation of electric vehicles. The RDE estimation is affected …

We develop a new k-means clustering algorithm for data streams of points from a Euclidean
space. We call this algorithm StreamKM++. Our algorithm computes a small weighted …

We investigate variants of Lloyd's heuristic for clustering high-dimensional data in an attempt
to explain its popularity (a half century after its introduction) among practitioners, and in …

Seeding-the task of finding initial cluster centers-is critical in obtaining high-quality
clusterings for k-Means. However, k-means++ seeding, the state of the art algorithm, does …

Clustering is a fundamental problem in statistics and machine learning. Lloyd's algorithm,
proposed in 1957, is still possibly the most widely used clustering algorithm in practice due …

In optimization or machine learning problems we are given a set of items, usually points in
some metric space, and the goal is to minimize or maximize an objective function over some …

The quality of K-Means clustering is extremely sensitive to proper initialization. The classic
remedy is to apply k-means++ to obtain an initial set of centers that is provably competitive …