tree-frog

Ven the villagers not too sure, but rumors this tree has a bee nest, the villagers use the cellular fire, even the results of this tree along the fire, the final will Shaochuan a hole; It was also said to be the termites in the borers However, this tree is very strong vitality, still continue to grow.

ffect of tree called the tree is a very big hole， but is why 就连村民也不太清楚，但传闻这棵树有一个蜜蜂巢，村民用火烧这个蜂巢，结果连这棵树也一并起火，最后便烧穿了一个洞；也有人说是被白蚁蛀的，但这棵树生命力非常顽强，仍然不断继续生长。

Two approaches,one called CG method in this paper which based spatial cubic partitioning where the axis-aligned bounding box of the data points is partitioned by a cubical grid,and another based on tree structure such as kd tree are known for the problem.

目前主要有2种方法，一种是基于立方体分割形成的三维立方体体素索引数组的体素栅格（CG方法，另一种方法是基于树索引结构的方法如kd-Tree等。

A tree hugger is someone who really likes trees and the environment and is willing to do pretty much anything to protect them.

我也猜想 a tree hugger 大概也就是特别喜欢树木、关心环保的人囉。Larry，tree hugger 这个名词是有讚扬的意思，还是含有贬义啊？

An efficient algorithm based on FP-tree and support array for mining maximal frequent itemsets is proposed. At first the FP-tree and the support array are created at the same time. Then a maximal frequent itemsets tree MAXFP-tree is built up to store all the maximal frequent itemsets. Therefore, it can reduce the search space and improve the efficiency of the algorithm.

提出了一个基于频繁模式树即FP-tree和支持度数组相结合的最大频繁项集挖掘算法，首先建立FP-tree，同时建立支持度数组，然后在此基础上建立最大频繁项集树MAXFP-tree，MAXFP-tree中包含了所有最大频繁项集，缩小了搜索空间，提高了算法的效率。

Then the spatial index is constructed which includes not only the external approximate expression but also interior approximate expression of spatial object and is extended from tranditional index only with external approximate expression. The index structures of MR-tree and MRD-tree based on multi-approximate expression are set up with the prototype index of R-tree, and the relevant algorithms about the index of insertion, deletion and search are provided. Then, the efficiencies of constructing index based on the multi-approximate expression and window range query are analysised and compared. In addition, the several algorithms and efficiency of soving the maximum enclosed circle and maximum enclosed rectangle are discussed and analyzed during constructing index based on multi-approximate expression.

首先，论文讨论了空间数据的特点、空间数据库的关键技术以及空间对象间的拓扑、距离以及方向关系，分析了空间对象的外部和内部近似表达，然后将传统索引中只利用空间对象的外部近似扩展为既有空间对象的外部近似又有空间对象的内部近似，即以空间对象的多重近似来构建索引，并以R-tree为索引原型构建基于多重近似的MR-tree和MRD-tree索引结构，然后给出了相关索引的插入、删除及查找算法，并通过实验比较分析了基于多重近似索引与基于外部近似索引的构建效率及窗口范围查询效率。

In the ER-Tree, the efficiency of retrieval could be improved by the ways of partition of the vector space with hyper-sphere and R*-Tree s re-insert technology introduced to enhance ER-Tree s capability to represent the data-sets'features.

该索引树用超球体划分多维向量空间，以有利于计算最近邻；吸取R*-Tree树的重插技术，以增强索引树对数据集整体特征的表达能力，从而提高检索效率；通过引入插入安全点和删除安全点概念，有效地提高建树的效率。

M sitting here I miss the power, I'd like to go out taking a shower, but there's a heavy cloud inside my head, I fell so tired put myself into bed, but nothing ever happens and I wander. I solation is not good for me, I solation I want to sit on a lemmon tree . I'm stepping around in adesert of joy, baby anyhow I'll got another toy, and everything will happen and you'll wander. I wander how I wander why ?

solation 期待下雨的一棵 Lemon Tree 你总是望著蓝蓝海面说著流浪过的梦容易实现这句话什么意思---我不清楚----爱多美丽充满香气只是在心里它总是酸溜溜地我不懂我自己越来越像 Lemon Tree------我一天一天更爱你我不管不管不管爱会苦苦地海蓝蓝的天气我的爱是 Lemon Tree 哦。。。。

Lemon tree 歌词:I'll sitting here in a boring room, It's just another rainy sunday afternoon, I'm wasting my time I got nothing to do, I'm hanging around I'm waiting for you. But nothing ever happens and I wander. I'm driving around in my car, I'd like to change my point of view, I feel so lonely I'm waiting for you, But nothing ever happens and I wander. I wander how I wander why yesterday you told me about blue blue sky; and all that I can see is just a yellow lemmon tree, I'm turning my head up and down I'm turning turning turning turning turning around and all that I can see is just another lemmon tree. see,dalada,diladada... I'm sitting here I miss the power, I'd like to go out taking a shower, but there's a heavy cloud inside my head, I fell so tired put myself into bed, but nothing ever happens and I wander. I solation is not good for me, I solation I want to sit on a lemmon tree . I'm stepping around in adesert of joy, baby anyhow I'll got another toy, and everything will happen and you'll wander. I wander how I wander why ?

中文翻译：一个人孤单单的下午当风吹得每棵树都想跳舞记得昨天你穿蓝色衣服你说对爱太专注容易孤独这句话什么意思我不清楚我爱上了云爱上你多么希望像你自由来去原来星期天容易思念反覆看部电影一遍一遍孤独的流著眼泪回忆太美爱多美丽充满香气只是在心里它总是酸溜溜地我不懂我自己越来越像 Lemon Tree 我一天一天更爱你我不管不管不管爱会苦苦地海蓝蓝的天气我的爱是 Lemon Tree 我爱上了云爱上你多么希望像你自由来去原来星期天容易想念反覆看部电影一遍一遍孤独的流著眼泪回忆太美 Isolation 想住进你心里 Isolation 期待下雨的一棵 Lemon Tree 你总是望著蓝蓝海面说著流浪过的梦容易实现这句话什么意思我不清楚爱多美丽充满香气只是在心里它总是酸溜溜地我不懂我自己越来越像 Lemon Tree 我一天一天更爱你我不管不管不管爱会苦苦地海蓝蓝的天气我的爱是 Lemon Tree 哦。。。。

Firstly the properties of L-MAX frequent itemsets is analysed.Then by extending FP-tree structure an ExFP-tree data structure is presented and the algorithm of ExFP-tree generated is given.According to the character of prefixing paths shared in FP-tree,ExFP-tree implements compressingly storage of transaction information with sharing the transaction information of descendant nodes.

ExFP-tree利用FP-tree共享前缀路径的性质通过共享子孙节点事务信息策略实现大量事务信息的压缩存储；最后基于FP-MAX算法，提出基于ExFP-tree的L-MAX频繁项集挖掘算法，核心思想是先根据L-MAX频繁项集长度约束性质进行前瞻剪枝再进行最大频繁项集挖掘，并通过回溯策略直接定位生成对应事务集。

In a database the concept of an example might change along with time which is known as concept drift When the concept drift the classification model built by use of old data is unsuitable for classify new data Therefore concept drift has become a hot issue in data mining in recent years Although many algorithms had been proposed to resolve this problem they can not provide users with the reason of concept drift However a user might be very interested in such rules For example doctors want to find what makes disease change; researchers want to know the reason of the variety of the weather; and decision makers would like to understand why a customer's shopping habit change In this thesis we propose a Concept Drift Rule mining Tree called CDR-Tree to solve this problem CDR-Tree can not only find the rule of concept drift also build the prediction model for both old and new data at the same time

无论在大型资料库或现实生活中，同一资料样本的概念有可能会随著时间的递移而改变，也就是产生所谓的概念漂移。当样本发生概念漂移时，由旧有资料所建构的分类模组将不再适用於预测新获得的资料，因此，近年来概念漂移已成为资料探勘中一项热门的研究议题。虽然已有？多学者提出不同的技术来解决概念漂移的问题，但是这些方法都是利用修正或重建的方式来产生适合新资料的预测模组，并无法提供造成概念漂移的原因。然而对使用者而言，其感兴趣的可能正是这些引起概念漂移的规则，如医生可能想了解引起疾病变化的主因、学者会想要知道气候转变的规则、或是决策者想找出顾客购物习惯改变的因素等。因此，本论文提出概念漂移规则探勘树( Concept Drift Rule mining Tree )，简称CDR-Tree，来解决这个问题。CDR-Tree不但能探测出造成概念漂移的主要原因，亦能同时建立新旧资料的预测模组以供决策者运用使用。

For a big chunk of credit-card losses; the number of filings (and thus charge-off rates) would be rising again, whether

年美国个人破产法的一个改动使得破产登记急速下降，而后引起了信用卡大规模的亏损。

Eph. 4:23 And that you be renewed in the spirit of your mind

弗四23 而在你们心思的灵里得以更新