Queen's University

Dr. Alan Ableson

Assistant Professor, Department of Mathematics and Statistics
BIOINFORMATICS, COMPUTATIONAL MODELS, MATHEMATICS AND STATISTICS, LIFE & PHYSICAL SCIENCES
LinkedIn profile for Dr. Alan Ableson

Autobiography

I am an Assistant Adjunct Professor in the Department of Mathematics and Statistics at Queen's University. My research interests include: Bioinformatics; Applied Mathematics and Computational Models; and Applied Statistics.

Publications
Patents and Patent Applications

 

[photo credit: Queen's University Communications]

Most Recent Project

Efficient Statistical Pruning of Association Rules

Association mining is the comprehensive identification of frequent patterns in discrete tabular data. The result of association mining can be a listing of hundreds to millions of patterns, of which few are likely of interest. In this paper we present a probabilistic metric to filter association rules that can help highlight the important structure in the data. The proposed filtering technique can be combined with maximal association mining algorithms or heuristic association mining algorithms to more efficiently search for interesting association rules with lower support.

Keep Reading...

Other Projects

  • Method and apparatus for determining multi-dimensional structure

    This invention relates to methods and apparatus for determining the multi-dimensional topology of a substance (system) within a volume (space). A method according to a preferred embodiment of the invention comprises the steps of: acquiring a set of relative values for the density (scalar properties) of the volume, each value for a given location (point) within the volume; interpolating a set of functions to generate a continuous relative density for the volume; identifying critical points of the continuous relative density by using an eigenvector following method; and associating critical points with one another by following a gradient path of the continuous relative density between the critical points, The method is applicable to a wide range of data relating to fields such as crystallography, fluid dynamics, edge detection, and financial markets, to determine the topology of structures contained therein.

    Keep Reading...