叶子结点

此算法主要利用事务数据库建立事务树的方法，由叶子结点反向回溯，导出频繁集。

This algorithm establish Transaction-Tree which is based on Transaction database , create frequent itemsetby backdate reversely from leaf nodes .

而一般的搜索引擎在对博弈树进行剪枝的时候仅仅依据的是叶子结点评估值，没有考虑残局模式。

The ordinary search engines select the best move based on the position values of leaf nodes of game tree , without considering the endgame patterns .

改进B~+树算法的核心思想是让非叶子结点可含有的最多关键字个数是叶子结点的N倍。

The central idea of improved B ~ + tree algorithm is to allow the number of keys in non-leaf node is N times of leaf node .

在时空数据集中，通过对中间结点的剪枝，使得R-tree中的多个叶子结点可以同时计算其最近邻，从而减少运行时间，提高效率。

Through pruning the middle nodes , the multi-leaf nodes of the R-tree can compute the nearest neighbors at the same time , so as to reduce the runtime and improve the efficiency .

利用生成的网站结构树可以对网站的内容页面（即结构树的叶子结点）进行聚类，最后进行信息抽取，大大提高抽取的准确率与召回率。

The spanning tree can be used to cluster the content pages and to extract information so that precision and recall will be improved .

设计了一套新的叶子结点编码方式，基于该编码，实现了编码四叉树的邻域寻找。

A new leaf-coding algorithm in quadtree was designed . Based on the coding , the algorithm for neighbor searching in leaf-coding quadtree was implemented .

建树又可分为3个子过程：频率统计、创建叶子结点单链表、叶子结点连接成树。

Contribution can be divided into three sub-processes : frequency statistics to create a single linked list of leaf nodes , leaf nodes connected into a tree .

该算法将聚类结果表现为一棵多叉树，其中叶子结点代表文档，非叶子结点代表类簇。

This algorithm clustering results are showed as a multi-tree , in which the leaf nodes represents the documents , and the non-leaf node represents the class of cluster .

这种方法既保留了基于小波包完全分解干扰抑制算法不用设定能量聚集度门限的优点，也克服了基于小波包完全分解中的门限需要由不受干扰的叶子结点来提供的缺点。

This algorithm dose not need to set energy concentration threshold as the algorithm based on wavelet packet complete decomposition ( WPCD ), and overcomes the disadvantage of setting threshold by non-interfered leaf nodes in WPCD algorithm .