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Fast Algorithms for Constructing Maximum Entropy Summary Trees

机译:构造最大熵汇总树的快速算法

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Karloff and Shirley recently proposed "summary trees" as a new way to visualize large rooted trees (Eurovis 2013) and gave algorithms for generating a maximum-entropy k-node summary tree of an input n-node rooted tree. However, the algorithm generating optimal summary trees was only pseudo-polynomial (and worked only for integral weights); the authors left open existence of a polynomial-time algorithm. In addition, the authors provided an additive approximation algorithm and a greedy heuristic, both working on real weights. This paper shows how to construct maximum entropy fc-node summary trees in time O(k~2n + n log n) for real weights (indeed, as small as the time bound for the greedy heuristic given previously); how to speed up the approximation algorithm so that it runs in time O(n + (k~4/∈) log(k/∈)), and how to speed up the greedy algorithm so as to run in time O(kn + n log n). Altogether, these results make summary trees a much more practical tool than before.
机译:Karloff和Shirley最近提出了“摘要树”作为可视化大根树的新方法(Eurovis 2013),并给出了用于生成输入n节点根树的最大熵k节点摘要树的算法。但是,生成最佳摘要树的算法只是伪多项式(并且仅适用于整数权重);作者保留了多项式时间算法的存在。此外,作者还提供了一个加法逼近算法和一个贪婪启发式算法,它们都在实际权重上起作用。本文展示了如何在时间O(k〜2n + n log n)内构造最大熵fc节点概要树(实际上,其大小与先前给出的贪婪启发式的时间一样小);如何加快逼近算法的运行时间O(n +(k〜4 /∈)log(k /∈)),以及如何加快贪婪算法的运行时间O(kn + n登录n)。总之,这些结果使摘要树比以前更实用。

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