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abstract: ‘Three algorithms are developed to explore dependency structure within and across multiple massive datasets using a stochastic sampling strategy in order to discover common and/or uncommon patterns, and in the process to capture the statistical significance of each association. These algorithms are ideal for collaborative learning by working groups involving practitioners in order to detect interesting patterns, find meaningful associations and generate new hypotheses. The first algorithm is a simple, yet efficient, algorithm and the second one is based on an adaptation of the Local Information Preserving (LIP) algorithm developed by Zhang \[*Master Thesis, University of British Columbia, Canada, 2010*\] to incorporate a dependency structure among the dataset components. The third algorithm is based on a distance-based dependence structure, and the algorithm is shown to be equivalent to the LIP algorithm when there is no dependency structure among the components. Extensive simulation studies are performed, and empirical studies of the algorithms are demonstrated, all in a series of well-specified problem settings.’
author:
– |
Hamid Rezaei\
Department of Statistics\
University of British Columbia\
Canada\
`rezaeh@stat.ubc.ca`\
Andrew Ng\
Department of Statistics\
University of British Columbia\
Canada\
`andrew.ng@stat.ubc.ca`\
Sébastien G. Wagner\
Department of Mathematics\
University of British Columbia\
Canada\
`sebastien.wagner@gmail.com`\
`Arash.Kazemzadeh@ubc.ca`
bibliography:
– ‘Papers.bib
https://colab.research.google.com/drive/1e2j9_2qagtjIwFIPP205psgTGIjawosy
https://colab.research.google.com/drive/1K4aj44QF0vF8VqS8ci6VV3LvNRdoBrPl
https://colab.research.google.com/drive/1l1V1eL2ogUkPlDydEA4q0ZZWdbe47TEU
https://colab.research.google.com/drive/13_uQItTLdz5KremXIPBvXpob-YxUxPNg
https://colab.research.google.com/drive/1B_DOG8FzKCAx3M1trAgKk39o2BUI-FOZ
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