Identifying Character Non-independence In
Phylogenetic Data Using Parallelized Rule
Induction From Coverings
Author(s)
J. Leopold, A. Maglia, M. Thakur, B. Patel & F. Ercal
Abstract
Undiscovered relationships in a data set may confound analyses, particularly
those that assume data independence. Such problems occur when characters used
for phylogenetic analyses are not independent of one another. Although a data
mining technique known as rule induction from coverings has earlier been shown
to be a promising approach for identifying such non-independence, its inherent
computational complexity has limited its application for large phylogenetic data
sets. Herein we present a parallelized implementation of the rule induction from
coverings strategy which overcomes some of these limitations. We also discuss
two heuristics that have been applied to the algorithm to further improve its
efficiency.
Keywords: data mining, phylogenetics, parallelization.
1 Introduction
Some types of data analyses require and/or assume independence between items
in the data set. If this assumption is violated, the results of the analysis may be
incorrect. For example, such a problem can occur in phylogenetic analyses (i.e.,
the reconstruction of evolutionary interrelationships between biological species).
A phylogenetic data set consists of rows representing different taxa and columns
representing characters or attributes of the taxa. Phylogenetic inference methods
such as maximum likelihood and parsimony are based on the assumption that
each character in the data set serves as an independent hypothesis of evolution
[1, 2]. If this assumption is not true, then correlated or non-independent
characters can effectively be overweighted in analyses [3], and the resulting
Keywords
data mining, phylogenetics, parallelization.
Related Book
Other papers in this volume
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