Dynamic Succinct Ordered Trees

2009 ◽  
pp. 439-450 ◽  
Author(s):  
Arash Farzan ◽  
J. Ian Munro
Keyword(s):  
Ordered Trees

scholarly journals Extremal Values of the Sackin Tree Balance Index

2021 ◽  
Author(s):  
Mareike Fischer
Keyword(s):  
Phylogenetic Trees ◽  
Theoretical Computer Science ◽  
Search Trees ◽  
Formal Proofs ◽  
Birth Process ◽  
Ordered Trees ◽  
Research Areas ◽  
New Findings ◽  
Mathematical Phylogenetics ◽  
Extremal Values

AbstractTree balance plays an important role in different research areas like theoretical computer science and mathematical phylogenetics. For example, it has long been known that under the Yule model, a pure birth process, imbalanced trees are more likely than balanced ones. Also, concerning ordered search trees, more balanced ones allow for more efficient data structuring than imbalanced ones. Therefore, different methods to measure the balance of trees were introduced. The Sackin index is one of the most frequently used measures for this purpose. In many contexts, statements about the minimal and maximal values of this index have been discussed, but formal proofs have only been provided for some of them, and only in the context of ordered binary (search) trees, not for general rooted trees. Moreover, while the number of trees with maximal Sackin index as well as the number of trees with minimal Sackin index when the number of leaves is a power of 2 are relatively easy to understand, the number of trees with minimal Sackin index for all other numbers of leaves has been completely unknown. In this manuscript, we extend the findings on trees with minimal and maximal Sackin indices from the literature on ordered trees and subsequently use our results to provide formulas to explicitly calculate the numbers of such trees. We also extend previous studies by analyzing the case when the underlying trees need not be binary. Finally, we use our results to contribute both to the phylogenetic as well as the computer scientific literature using the new findings on Sackin minimal and maximal trees to derive formulas to calculate the number of both minimal and maximal phylogenetic trees as well as minimal and maximal ordered trees both in the binary and non-binary settings. All our results have been implemented in the Mathematica package SackinMinimizer, which has been made publicly available.

scholarly journals Lattice-based similarity measures between ordered trees

1982 ◽  
Vol 25 (3) ◽  
pp. 206-225 ◽  
Author(s):  
Stephen C Hirtle
Keyword(s):  
Similarity Measures ◽  
Ordered Trees

Edit Distance between Unlabelled Ordered Trees

2004 ◽  
pp. 257-259
Author(s):  
Anne Micheli ◽  
Dominique Rossin
Keyword(s):  
Edit Distance ◽  
Ordered Trees

scholarly journals Enumerating alternating tree families

2008 ◽  
Vol DMTCS Proceedings vol. AJ,... (Proceedings) ◽  
Author(s):  
Markus Kuba ◽  
Alois Panholzer
Keyword(s):  
Nous Obtenons ◽  
Asymptotic Results ◽  
Root Node ◽  
Ordered Trees ◽  
Exponential Generating Function ◽  
Random Node ◽  
Enumeration Problems ◽  
International Audience ◽  
Nous Examinons ◽  
Labelled Trees

International audience We study two enumeration problems for $\textit{up-down alternating trees}$, i.e., rooted labelled trees $T$, where the labels $ v_1, v_2, v_3, \ldots$ on every path starting at the root of $T$ satisfy $v_1 < v_2 > v_3 < v_4 > \cdots$. First we consider various tree families of interest in combinatorics (such as unordered, ordered, $d$-ary and Motzkin trees) and study the number $T_n$ of different up-down alternating labelled trees of size $n$. We obtain for all tree families considered an implicit characterization of the exponential generating function $T(z)$ leading to asymptotic results of the coefficients $T_n$ for various tree families. Second we consider the particular family of up-down alternating labelled ordered trees and study the influence of such an alternating labelling to the average shape of the trees by analyzing the parameters $\textit{label of the root node}$, $\textit{degree of the root node}$ and $\textit{depth of a random node}$ in a random tree of size $n$. This leads to exact enumeration results and limiting distribution results. Nous étudions deux problèmes de dénombrement d'$\textit{arbres alternés haut-bas}$ : par définition, ce sont des arbres munis d'une racine et tels que, pour tout chemin partant de la racine, les valeurs $v_1,v_2,v_3,\ldots$ associées aux nœuds du chemin satisfont la chaîne d'inégalités $v_1 < v_2 > v_3 < v_4 > \cdots$. D'une part, nous considérons diverses familles d'arbres intéressantes du point de vue de l'analyse combinatoire (comme les arbres de Motzkin, les arbres non ordonnés, ordonnés et $d$-aires) et nous étudions pour chaque famille le nombre total $T_n$ d'arbres alternés haut-bas de taille $n$. Nous obtenons pour toutes les familles d'arbres considérées une caractérisation implicite de la fonction génératrice exponentielle $T(z)$. Cette caractérisation nous renseigne sur le comportement asymptotique des coefficients $T_n$ de plusieurs familles d'arbres. D'autre part, nous examinons le cas particulier de la famille des arbres ordonnés : nous étudions l'influence de l'étiquetage alterné haut-bas sur l'allure générale de ces arbres en analysant trois paramètres dans un arbre aléatoire (valeur de la racine, degré de la racine et profondeur d'un nœud aléatoire). Nous obtenons alors des résultats en terme de distribution limite, mais aussi de dénombrement exact.

Clustering Rooted Ordered Trees

2007 ◽  
Author(s):  
Mostafa Haghir Chehreghani ◽  
Masoud Rahgozar ◽  
Caro Lucas ◽  
Morteza Haghir Chehreghani
Keyword(s):  
Ordered Trees

Segmental Mapping and Distance for Rooted Labeled Ordered Trees

2012 ◽  
pp. 485-494 ◽  
Author(s):  
Tomohiro Kan ◽  
Shoichi Higuchi ◽  
Kouichi Hirata
Keyword(s):  
Ordered Trees

Tree Pattern Matching

1997 ◽  
Author(s):  
K. Zhang ◽  
D. Shasha
Keyword(s):  
Point Of View ◽  
Programming Approach ◽  
Suffix Trees ◽  
Dynamic Programming Approach ◽  
Ordered Trees ◽  
Single Leaf ◽  
Tree Pattern Matching ◽  
A Cell ◽  
Carry Over ◽  
Don't Cares

Most of this book is about stringology, the study of strings. So why this chapter on trees? Why not graphs or geometry or something else? First, trees generalize strings in a very direct sense: a string is simply a tree with a single leaf. This has the unsurprising consequence that many of our algorithms specialize to strings and the happy consequence that some of those algorithms are as efficient as the best string algorithms. From the point of view of “treeology”, there is the additional pragmatic advantage of this relationship between trees and strings: some techniques from strings carry over to trees, e.g., suffix trees, and others show promise though we don’t know of work that exploits it. So, treeology provides a good example area for applications of stringologic techniques. Second, some of our friends in stringology may wonder whether there is some easy reduction that can take any tree edit problem, map it to strings, solve it in the string domain and then map it back. We don’t believe there is, because, as you will see, tree editing seems inherently to have more data dependence than string editing. (Specifically, the dynamic programming approach to string editing is always a local operation depending on the left, upper, and upper left neighbor of a cell. In tree editing, the upper left neighbor is usually irrelevant — instead the relevant cell depends on the tree topology.) That is a belief not a theorem, so we would like to state right at the outset the key open problem of treeology: can all tree edit problems on ordered trees (trees where the order among the siblings matters) be reduced efficiently to string edit problems and back again?. The rest of this chapter proceeds on the assumption that this question has a negative response. In particular, we discuss the best known algorithms for tree editing and several variations having to do with subtree removal, variable length don’t cares, and alignment. We discuss both sequential and parallel algorithms.

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