The problem of comparing phylogenetic trees is based on finding a distance between different models of evolution. This problem is important because of existing various methods for reconstructing phylogenies, which app...
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The problem of comparing phylogenetic trees is based on finding a distance between different models of evolution. This problem is important because of existing various methods for reconstructing phylogenies, which applied to the same data set result in different trees. In the paper we construct a measure in a polynomial time using new structures called i-clusters. We analyze properties of i-clusters and prove that the measure is a metric in some space of rooted phylogenetic trees. The constructed measure is parametrized and the scalability parameter may be defined by user according to the real data size and complexity. The presented measure is the extension of widely applied error metric, defined by Robinson and Foulds in 1981. The generalization enables us to ommit small mistakes on low level and not to loose similarity of compared trees on high level.
This paper introduces a fractal partition class defined over a unit wrapped space. The cells of a fractal partition have self-similarity property and can be used as a set of tiles that can tile R n space periodically...
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This paper introduces a fractal partition class defined over a unit wrapped space. The cells of a fractal partition have self-similarity property and can be used as a set of tiles that can tile R n space periodically or quasi-periodically with non-uniform tiling density. An algorithm for generating set of fractal cells using Voronoi diagram computation is proposed with several applications in computer graphics and computational chemistry.
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