Dr. Daniel Aloise
Polytechnique Montréal
Dr Daniel Aloise is an assistant professor at the Computer Engineering Department at Polytechnique Montréeal. He obtained his PhD in Applied Maths from Polytechnique Montréeal in 2009 and he has been assistant professor at Universidade Federal do Rio Grande do Norte, Brazil, in 2009-2016. His research interests include data mining, optimization, mathematical programming and how these disciplines interact to tackle problems in the Big Data era. Daniel has published in leading operations research and data mining journals including Machine Learning, Pattern Recognition, European Journal of Operational Research, Mathematical Programming and Journal of Global Optimization.



Journal papers

D. Aloise, C. Nielsen Damasceno, N. Mladenovic, D. N. Pinheiro, 2016. On strategies to fix degenerate k-Means-means solutions. Journal of Classification

E. Santi, D. Aloise, S, J Blanchard, 2016. A model for clustering data from heterogeneous dissimilarities. European Journal of Operation Research, 253 (3), 659-672.

S. J. Blanchard, D. Aloise, W. S. DeSarbo, 2016. Extracting Summary Piles from Sorting Task Data. Journal of Marketing Research In-Press. doi:

D. Aloise, A. Araújo, 2015. A derivative-free algorithm for refining numerical microaggregation solutions. International Transactions in Operational Research, 22 (4) 693-712. DOI: 10.1111/itor.12125

Working papers

L. R. Costa, D. Aloise, N. Mladenovic, 2016. Less is More Approach for Balanced Minimum Sum-Of-Squares Clustering. G-2016-80

P. Artem, D. Aloise, M. Mladenovic, 2016. NP-Hardness of Balanced Minimum Sum-Of-Squarres Clustering. G-2016-79

D. Dzamic, D. Aloise, N. Mladenovic, 2016. Ascent — Descent Variable Neighborhood Decomposition Search for Community Detection by Modularity Maximization. G-2016-35

D. Aloise, C. Contardo, 2016. An Iterative Algorithm for the Solution of very Large-Scale Diameter Clustering Problems. G-2015-140