The k-means algorithm is a well-known method for partitioning n points that lie in the d-dimensional
space into k clusters. Its main features are simplicity and speed in practice. …

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 …

Greedy algorithms are practitioners’ best friends—they are intuitive, are simple to implement,
and often lead to very good solutions. However, implementing greedy algorithms in a …

Hartigan’s method for $ k $-means clustering is the following greedy heuristic: select a point,
and optimally reassign it. This paper develops two other formulations of the heuristic, one …

We show that k-means clustering is an NP-hard optimization problem, even for instances in
the plane. Specifically, the hardness holds for k= Θ (nϵ), for any ϵ> 0, where n is the number …

The present work deals with the problem of information acquisition in a strategic networked
environment. To study this problem, Kleinberg and Raghavan (FOCS 2005) introduced the …

Multi-fold cross-validation is an established practice to estimate the error rate of a learning
algorithm. Quantifying the variance reduction gains due to cross-validation has been …

One of the most popular algorithms for clustering in Euclidean space is the $k$-means
algorithm; $k$-means is difficult to analyze mathematically, and few theoretical guarantees are …

The secretary problem lies at the core of mechanism design for online auctions. In this work
we study the generalization of the classical secretary problem in a setting where there is only …

Choosing a subgraph that can concisely represent a large real-world graph is useful in many
scenarios. The usual strategy employed is to sample nodes so that the induced subgraph …