Clustering under constraints is a recent innovation in the artificial intelligence community that has yielded significant practical benefit. However, recent work has shown that fo...
We resolve the problem of small-space approximate selection in random-order streams. Specifically, we present an algorithm that reads the n elements of a set in random order and ...
This paper investigates the design of parallel algorithmic strategies that address the efficient use of both, memory hierarchies within each processor and a multilevel clustered ...
Frank K. H. A. Dehne, Stefano Mardegan, Andrea Pie...
Abstract. We present approximation algorithms for almost all variants of the multicriteria traveling salesman problem (TSP), whose performances are independent of the number k of c...
Most clustering algorithms produce a single clustering for a given data set even when the data can be clustered naturally in multiple ways. In this paper, we address the difficult...