Evolutionary algorithms for grouping high dimensional Email data

Article type: Research Article

Authors: Counsell, Steve^a | Liu, Xiaohui^b | McFall, Janet^c | Swift, Stephen^{b; *} | Tucker, Allan^b

Affiliations: [a] School of Computer Science and Information Systems, Birkbeck College, University of London, Malet Street, London, UK. E-mail: [email protected] | [b] Department of Information Systems and Computing, Brunel University, Uxbridge, UK. Tel.: +44 1895 816240, +44 1895 816253, +44 1895 816253; E-mail: [email protected], [email protected], [email protected] | [c] The Building Crafts College, Stratford, London, UK

Correspondence: [*] Corresponding author. Tel.: +44 1895 816253

Abstract: Grouping problems arise in many industrial and medical applications; examples include bin packing, workshop layout design, and graph colouring. This type of problem has been successfully handled using Grouping Genetic Algorithms. However in problems where there are perhaps thousands of objects to be grouped, we have found that Genetic Algorithm approaches can run into problems. This paper continues our research into a method we have developed for decomposing a large number of objects into mutually exclusive subsets where within-group dependencies are high and between-group dependencies are low. The method uses an Evolutionary Algorithm approach but where the whole population is a solution to the grouping problem rather than considering many candidate solutions. This reduces the resource overheads during computer implementation and the results are promising when compared with standard statistical methods and a Hill Climbing algorithm, all applied to email log file data.

Keywords: clustering, grouping, evolutionary computation, electronic mail log files

DOI: 10.3233/IDA-2002-6603

Journal: Intelligent Data Analysis, vol. 6, no. 6, pp. 503-516, 2002