Large-Scale Data Analysis Using Heuristic Methods

Article type: Research Article

Authors: Dzemyda, Gintautas | Sakalauskas, Leonidas

Affiliations: Vilnius University Institute of Mathematics and Informatics, Akademijos 4, LT-08663 Vilnius, Lithuania, E-mail: [email protected]

Abstract: Estimation and modelling problems as they arise in many data analysis areas often turn out to be unstable and/or intractable by standard numerical methods. Such problems frequently occur in fitting of large data sets to a certain model and in predictive learning. Heuristics are general recommendations based on practical statistical evidence, in contrast to a fixed set of rules that cannot vary, although guarantee to give the correct answer. Although the use of these methods became more standard in several fields of sciences, their use for estimation and modelling in statistics appears to be still limited. This paper surveys a set of problem-solving strategies, guided by heuristic information, that are expected to be used more frequently. The use of recent advances in different fields of large-scale data analysis is promoted focusing on applications in medicine, biology and technology.

Keywords: heuristics, robust statistics, Markov model, regression, clustering, visualization

Journal: Informatica, vol. 22, no. 1, pp. 1-10, 2011