Daniel Aloise

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Cahiers du GERAD

34 results — page 2 of 2

G-2015-140
An iterative algorithm for the solution of very large-scale diameter clustering problems
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We introduce an iterative algorithm for the solution of the diameter minimization clustering problem (DMCP). Our algorithm is based upon two observations: 1)...

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G-2015-18
A model for clustering data from heterogeneous subjects
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Clustering is a data mining method which consists in partitioning a given set of n objects into p clusters in order to minimize the dissimilarity among o...

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G-2011-59
Modularity Maximization in Networks by Variable Neighborhood Search
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Finding communities, or clusters, in networks, or graphs, has been the subject of intense studies in the last ten years. The most used criterion for that pu...

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G-2011-18
On the Weber Facility Location Problem with Limited Distances and Side Constraints
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The objective in the continuous facility location problem with limited distances is to minimize the sum of distance functions from the facility to the cust...

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G-2010-48
A Variable Neighborhood Search Heuristic for Normalized Cut Clustering
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Normalized cut is one of the most popular graph clustering criteria. The main approaches proposed for its resolution are spectral clustering methods (e.g. [1...

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G-2010-36
Column Generation Algorithms for Exact Modularity Maximization in Networks
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Finding modules, or clusters, in networks currently attracts much attention in several domains. The most studied criterion for doing so, due to Newman and Gi...

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G-2009-25
An Improved Column Generation Algorithm for Minimum Sum-of-squares Clustering
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Given a set of entities associated with points in Euclidean space, minimum sum-of-squares clustering (MSSC) consist in partitioning this set into clusters su...

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G-2009-09
Exploring Adaptive Memory and Vocabulary Building to Improve a Multistart Heuristic for a Multicommodity Network Design Problem
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This paper focuses on the use of different memory strategies to improve multistart methods. A network design problem in which the costs are given by discrete...

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G-2008-40
A Branch-and-Cut SDP-Based Algorithm for Minimum Sum-of-Squares Clustering
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Minimum sum-of-squares clustering (MSSC) consists in partitioning a given set of <i>n</i> points into <i>k</i> clusters in order to minimize the sum of squar...

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G-2008-34
Clustering - A Chapter for Handbook of Discrete and Combinatorial Optimization
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Methods, models, heuristic and exact algorithms for clustering are reviewed from a mathematical programming view point.

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G-2008-33
NP-Hardness of Euclidean Sum-of-Squares Clustering
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A recent proof of NP-hardness of Euclidean sum-of-squares clustering, due to Drineas et al., <i>Machine Learning</i> 56, 9--33, 2004, is not valid. An altern...

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G-2008-26
Evaluating a Branch-and-Bound RLT-Based Algorithm for Minimum Sum-of-Squares Clustering
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Minimum sum-of-squares clustering consists in partitioning a given set of <i>n</i> points into <i>c</i> clusters in order to minimize the sum of squared dist...

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G-2007-50
On the Complexity of Minimum Sum-of-Squares Clustering
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To the best of our knowledge, the complexity of minimum sum-of-squares clustering is unknown. Yet, it has often been stated that this problem is NP-hard. We...

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G-2006-02
A Hybrid Heuristic for a Real-Life Car Sequencing Problem with Painting and Assembly Line Constraints
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We address a multi-objective version of the car sequencing problem, which consists in sequencing a given set of cars to be produced in a single day, minimizi...

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