大侠，帮忙翻译一段文字！！！

Question

在信息技术飞速发展的今天，文本是我们获取信息的主要来源。然而面对海量、异构的文本信息人们往往的显的束手无策。因此，寻找一种行之有效的文本聚类算法已成为一个重要的研究课题。
本文深入研究了几种常用的聚类方法和几种文本数学模型的构建，重点研究了K均值算法及其在文本聚类中的扩展算法—超球K均值算法(SKM算法)。该算法用Salton教授提出的向量空间模型把文本表示成不可视的超球向量空间的一个向量，采用一定的预处理手段(降维、同义词归并、停用词过滤)完成维数约减，使用余弦相似度度量文本之间的相似度，并选择总凝聚度作为目标收敛函数，通过有限次的迭代达到局部最优，输出聚类结果。实验结果验证了SKM算法的有效性和可行性，进一步表明SKM算法简单、高效，十分适合文本挖掘。

Answer 1

楼上的兄弟你太蠢了，如果机器翻译“绝对权威，绝对专业”的话，就没有人去花钱花力气学外语了，想你这样的蠢货就可以给胡哥当翻译了。

Answer 2

Today developing at full speed in IT, main part being that we gain information stems from a version. But version information people who faces great capacity for liquor , the isomerism sometimes obvious no way out. Therefore, seek one kind of effective version cluster-algorithm already becoming a important go into problem. The structure going deep into the mathematic model having studied several kinds clustering method and several kinds versions in common use has built the main body of a book , priority has studied K mean value algorithm and their expansion algorithm in version clustering surpasses the ball K mean value algorithm (SKM algorithm). A vector owing an algorithm the ball vector space a version is expressed with vector space model brought forward by Salton professor becoming being over being not allowed to look at, adopt certain pretreatment means (conquer the thinking , synonym conflation , stop verbal fliteration) to accomplish a dimension cutting about , uses the similarity degree between degree of amounts cosine similarity versions , chooses general coacervation degree and restraining self's a function as the target , gives preferential treatment most by the fact that inferior limited iteration reaches part, result exporting clustering. The experiment bear fruit has verified algorithmic validity of SKM and feasibility , has indicated the SKM algorithm simplicity further, high-effect, the version very suitable excavates.

Answer 3

LS差远了

Answer 4

看到高分还是很有用的,俺以后就这样请人帮忙了...