毕业论文主成分分析

因子得分*方差贡献率试试

职业病学毕业论文可以写当前比较热门的尘肺病。当时写的时候也是费了不少脑筋，还是上届的同学给的雅文网，那是帮了大忙啊广州市部分工厂工人职业病危害认知及影响因素的探讨浅议增强行业自律在我国职业病防治工作中的重要意义湖南省贯彻《职业病防治法》控制职业病危害近期效果及其影响因素的分析北京市西城区企业员工对职业病防治知识的知晓情况浅谈我国劳动保护与职业病防治2006—2010年广东省新发职业病病谱分析《中华人民共和国职业病防治法》实施前后合肥市诊断职业病资料分析2001—2010年广东省新发职业病分类分布特征分析宁夏部分企业职业病危害因素作业工人职业健康监护分析2003—2012年深圳市松岗街道职业病发病情况北京市西城区企业职工职业病防治知识知晓率调查分析唐山市重大职业病危害防范管理现状研究某煤电扩建工程职业病危害因素检测与分析职业病风险与雇主责任保险的风险管控浅谈职业病防治法中放射卫生工作的特殊管理3例职业病案例分析浙江省湖州市2008—2012年职业病报告病例分析 优先出版北京市职业病诊断与鉴定问题和对策深圳市光明新区2006～2011年职业病发病情况分析某公司乙醇胺装置建设项目职业病危害控制效果评价1例中毒性肝损害病例职业病诊断过程分析扬州市10年间职业病发病状况分析广西1992至2005年职业病发病情况的流行病学调查某机械公司新建项目职业病危害控制效果评价上海市闵行区19662004年职业病发病状况分析1992～2003年淄博市职业病发病情况分析广西2002年职业病发病报告与分析唐山市不同行业职业有害因素和职业病分析2007—2013年无锡市职业病发病情况分析

说实在话 不管你选什么题目 你是在毕业论这个前提下写的 毕业论文为的是啥 对普通大学生来讲还不是那个证（除非你真的很优秀 应聘的时候能准备把毕业论文作为炫耀的资本） 即使你不选DOTA为题 你选的那个题目难道毕业论文过后能被发表 然后又能实施么 那显然不可能 毕业论文主要是考察知识的运用能力 只要你选的那个题能最好的体现你的能力就行 就按你说的用成分分析和聚类等方法 看你怎么用了 方法用好了才是关键 如果你只是应付老师的话 可以写有利于提高大家对DOTA的认识啦 提高DOTA综合水平啦 以至于在国际对抗赛中取胜 为国家争的荣誉 电子竞技也属于体育竞技哦

你首先得跟你们导师说dota也是一门运动，比如足球等等它是体力运动，而dota呢它是脑力运动。也就是先要说明dota是有意义的。既然dota是一门运动，那么运动就有技巧，你这篇文章就是考试运动员就应该怎样运动，汽车你通过这些能运用上你的知识，就行了。

你的邮箱发不进去，请换一个，这里发部分供你参考Principal component analysisPrincipal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has as high a variance as possible (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is jointly normally distributed. PCA is sensitive to the relative scaling of the original variables. Depending on the field of application, it is also named the discrete Karhunen–Loève transform (KLT), the Hotelling transform or proper orthogonal decomposition (POD).PCA was invented in 1901 by Karl Pearson.[1] Now it is mostly used as a tool in exploratory data analysis and for making predictive models. PCA can be done by eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute. The results of a PCA are usually discussed in terms of component scores (the transformed variable values corresponding to a particular case in the data) and loadings (the weight by which each standarized original variable should be multiplied to get the component score) (Shaw, 2003).PCA is the simplest of the true eigenvector-based multivariate analyses. Often, its operation can be thought of as revealing the internal structure of the data in a way which best explains the variance in the data. If a multivariate dataset is visualised as a set of coordinates in a high-dimensional data space (1 axis per variable), PCA can supply the user with a lower-dimensional picture, a "shadow" of this object when viewed from its (in some sense) most informative viewpoint. This is done by using only the first few principal components so that the dimensionality of the transformed data is is closely related to factor analysis; indeed, some statistical packages (such as Stata) deliberately conflate the two techniques. True factor analysis makes different assumptions about the underlying structure and solves eigenvectors of a slightly different matrix.