We consider fc-median clustering in finite metric spaces and fc-means clustering in
Euclidean spaces, in the setting where k is part of the input (not a constant). For the fc …

A common approach to clustering data is to view data objects as points in a metric space,
and then to optimize a natural distance-based objective such as the k-median, k-means, or …

Given a point set S and an unknown metric d on S, we study the problem of efficiently
partitioning S into k clusters while querying few distances between the points. In our model …

We consider the model introduced by Bilu and Linial (2010)[13], who study problems for
which the optimal clustering does not change when distances are perturbed. They show that …

This study presents a Vehicle Routing Problem solved through some metaheuristics
procedures. A two-phase methodology was used for that: 1) defining clusters of demand …

We consider the model introduced by Bilu and Linial [12],, who study problems for which the
optimal clustering does not change when distances are perturbed. They show that even …

In this paper, we analyze the performance of a simple and standard Local Search algorithm
for clustering on well behaved data. Since the seminal paper by Ostrovsky, Rabani …

Clustering is a notoriously hard task. Recently, several papers have suggested a new
approach to clustering, motivated by examining natural assumptions that arise in practice, or …

This chapter introduces approximation stability, an input condition motivated by the common
practice of using the score of a solution under an easy-to-measure objective function as …

This thesis can be viewed as a collection of work in differential privacy and in clustering. In
its first part we discuss work aimed at preserving differential privacy in a social network, with …