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» Lower bounds and heuristic algorithms for the k
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ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
14 years 3 months ago
Worst-Case and Smoothed Analysis of k-Means Clustering with Bregman Divergences
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
Bodo Manthey, Heiko Röglin
APPROX
2009
Springer
118views Algorithms» more  APPROX 2009»
14 years 3 months ago
Hellinger Strikes Back: A Note on the Multi-party Information Complexity of AND
The AND problem on t bits is a promise decision problem where either at most one bit of the input is set to 1 (NO instance) or all t bits are set to 1 (YES instance). In this note...
T. S. Jayram
EUROPAR
2005
Springer
14 years 2 months ago
Replication Predicates for Dependent-Failure Algorithms
Abstract. To establish lower bounds on the amount of replication, there is a common partition argument used to construct indistinguishable executions such that one violates some pr...
Flavio Paiva Junqueira, Keith Marzullo
OPODIS
2004
13 years 9 months ago
Directional Versus Omnidirectional Antennas for Energy Consumption and k-Connectivity of Networks of Sensors
A network is k-connected if it remains connected after the removal of any k - 1 of its nodes. Assume that n sensors, modeled here as (omni)directional antennas, are dropped random...
Evangelos Kranakis, Danny Krizanc, Eric Williams
IPL
2010
93views more  IPL 2010»
13 years 7 months ago
Randomized algorithms for online bounded bidding
In the online bidding problem, a bidder is trying to guess a positive number T, by placing bids until the value of the bid is at least T. The bidder is charged with the sum of the...
Leah Epstein, Asaf Levin