# Daniel Aloise

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### 28 results — page 1 of 2

Distance metric learning algorithms aim to appropriately measure similarities and distances between data points. In the context of clustering, metric learnin...

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Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even mo...

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We consider the problem of designing vehicle routes in a distribution system that are at the same time cost-effective and visually attractive. In this pape...

BibTeX referenceThe Covering-Assignment Problem for swarm-powered ad-hoc clouds: A distributed 3D mapping use-case

The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perf...

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Clustering algorithms help identify homogeneous subgroups from data. In some cases, additional information about the relationship among some subsets of the d...

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This paper studies the Dynamic Facility Location Problem with Modular Capacities (DFLPM). We propose a linear relaxation based heuristic (LRH) and an evoluti...

BibTeX referenceConvex fuzzy \(k\)-medoids clustering

`\(K\)`

-medoids clustering is among the most popular methods for cluster analysis, but it carries several assumptions about the nature of the latent clusters...

Clustering is an automated and powerful technique for data analysis. It aims to divide a given set of data points into clusters which are homogeneous and/o...

BibTeX referenceThe carousel scheduling problem

Scheduling problems on which constraints are imposed with regard to the temporal distances between successive executions of the same task have numerous appli...

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In this article we consider a bi-objective vehicle routing problem in which, in addition to the classical minimization of the total routing cost, the operato...

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Clustering addresses the problem of finding homogeneous and well-separated subsets, called clusters, from a set of given data points. In addition to the poi...

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The `\(k\)`

-means is a benchmark algorithm used in cluster analysis. It belongs to the large category of
heuristics based on location-allocation steps that ...

The balanced clustering problem consists of partitioning a set of `\(n\)`

objects into `\(K\)`

equal-sized clusters as long as
`\(n\)`

is a multiple of `(K...

In this paper we propose a new variant of the Variable Neighborhood Decomposition Search (VNDS) heuristic for solving global optimization problems and apply ...

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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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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...

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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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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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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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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