A Linear-Time Majority Tree Algorithm

Amenta, Nina; Clarke, F. L.; John, Katherine St.

doi:10.1007/978-3-540-39763-2_17

Cited by 35 publications

(39 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MRT can be constructed in time that is linear in the total size of the input trees [3]. In contrast, while an MAST of two trees can be computed in polynomial time [4,47], finding an MAST of three or more trees is NP-hard if the trees have unbounded degree [4].…”

Section: Maximum Agreement Subtrees and Majority Rule Treesmentioning

confidence: 99%

EvoMiner: frequent subtree mining in phylogenetic databases

et al. 2013

View full text Add to dashboard Cite

The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the wellknown maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees ---two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set. Disciplines Theory and AlgorithmsThis article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/cs_techreports/11Noname manuscript No. The final publication is available at http://link.springer.com Abstract The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees -two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set.

show abstract

Section: Maximum Agreement Subtrees and Majority Rule Treesmentioning

confidence: 99%

EvoMiner: frequent subtree mining in phylogenetic databases

et al. 2013

View full text Add to dashboard Cite

show abstract

“…For any u ∈ V (T ), T [u] denotes the subtree of T rooted at the node u, so that Λ(T [u]) is the set of all leaf labels of leaves that are descendants of u. 2 The cluster collection of T is defined as Fig. 1 for an example.…”

Section: Definitions and Notationmentioning

confidence: 99%

“…As for randomized methods, Amenta et al [2] gave an algorithm with expected running time O(nk) but unbounded worst-case running time. Here, randomization is used to count and store the number of occurrences of clusters from S in suitably constructed hash tables.…”

Section: Previous Workmentioning

confidence: 99%

“…Existing phylogenetic trees may be based on different data sets or obtained by different methods, and do not always agree with each other; two trees can contain contradicting branching patterns even though their leaf label sets are identical. Also, when trying to infer a new, reliable phylogenetic tree from real data, heuristics for maximizing parsimony or resampling techniques such as bootstrapping may produce large collections of identically leaf-labeled phylogenetic trees having slightly different branching structures [2,3,7,8,19]. To deal with conflicts that arise between two or more such trees in a systematic manner, the concept of a consensus tree was invented [1,5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Algorithm for Building the Majority Rule Consensus Tree

Jansson

Shen

Sung

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. A deterministic algorithm for building the majority rule consensus tree of an input collection of conflicting phylogenetic trees with identical leaf labels is presented. Its worst-case running time is O(nk), where n is the size of the leaf label set and k is the number of input phylogenetic trees. This is optimal since the input size is Ω(nk). Experimental results show that the algorithm is fast in practice.

show abstract

“…One successful approach for finding a consensus of different evolutionary trees is to construct their maximum agreement subtree (MAST), which is the largest evolutionary tree that is a topological subtree of all the given trees. There are many algorithms proposed for constructing MAST; for example, [5,6,9,10,12,13,17], or more recently, [1,2,4,14].…”

Section: Introductionmentioning

confidence: 99%

From Constrained to Unconstrained Maximum Agreement Subtree in Linear Time

2007

View full text Add to dashboard Cite

We propose and study the Maximum Constrained Agreement Subtree (MCAST) problem, which is a variant of the classical Maximum Agreement Subtree (MAST) problem. Our problem allows users to apply their domain knowledge to control the construction of the agreement subtrees in order to get better results. We show that the MCAST problem can be reduced to the MAST problem in linear time and thus we have algorithms for MCAST with running times matching the fastest known algorithms for MAST.

show abstract

A Linear-Time Majority Tree Algorithm

Cited by 35 publications

References 12 publications

EvoMiner: frequent subtree mining in phylogenetic databases

EvoMiner: frequent subtree mining in phylogenetic databases

An Optimal Algorithm for Building the Majority Rule Consensus Tree

From Constrained to Unconstrained Maximum Agreement Subtree in Linear Time

Contact Info

Product

Resources

About