从海量数据中发现潜在标记指标, 须要借助多变量模式识别办法. 无监督的模式识别办法包含主成分剖析（PCA、聚类分析(HCE)等，依据模式识别模型抽提出对分类有重要奉献的指标后, 如果还须要进一步验证这些指标的差异性，那么能够在r语言中应用PLSDA模型进行剖析。

因为近期在剖析数据时用到了EM最大冀望预计法这个算法，在参数估计中也用到的比拟多。然而，发现国内在R软件上实现高斯混合散布的EM的实例并不多，大多数是对于1到2个高斯混合散布的实现，不易于推广，因而这里分享一下本人编写的k个高斯混合散布的EM算法实现请大神们多多指教。并联合EMCluster包对后果进行验算。

618购物狂欢节前后，网民较常搜寻的关键词在微博、微信、新闻三大渠道的互联网数据体现，同时通过剖析平台采集618相干媒体报道和消费者提及数据。

618购物狂欢节前后，网民较常搜寻的关键词在微博、微信、新闻三大渠道的互联网数据体现，同时通过剖析平台采集618相干媒体报道和消费者提及数据。

Twitter is a popular social network, where a lot of data waiting for our analysis. Twitter R package is a good tool for text mining of twitter data. This article is about how to use Twitter R package to get twitter data and import it into R, and then make some interesting data analysis.

Twitter is a popular social network, where a lot of data waiting for our analysis. Twitter R package is a good tool for text mining of twitter data. This article is about how to use Twitter R package to get twitter data and import it into R, and then make some interesting data analysis.

Twitter is a popular social network, where a lot of data waiting for our analysis. Twitter R package is a good tool for text mining of twitter data. This article is about how to use Twitter R package to get twitter data and import it into R, and then make some interesting data analysis.

A telephone company is interested in determining which customer characteristics are useful for predicting churn, customers who will leave their service. 

A telephone company is interested in determining which customer characteristics are useful for predicting churn, customers who will leave their service. 

原文链接：[链接]Objection：  {代码…} `Population Income Illiteracy Life Exp Murder HS Grad FrostAlabama 3615 3624 2.1 69.05 15.1 41.3 20Alaska 365 6315 1.5 69.31 11.3 66.7 152Arizona 2212 4530 1.8 70.55 7.8 58.1 15Arkansas 2110 3378 1.9 70.66 10.1 39.9 65California 21198 5114 1.1 71.71 10.3 62….