R语言中的数据挖掘算法
R是一种主要用于数据分析与数据可视化的语言及其操作环境。作为属于GNU系统的开源软件家族成员之一，并作为一个支持广泛的统计计算与数据可视化的优秀工具。
——百度百科
基于R语言的强大功能具备高效的数据处理能力，在数据分析等众多领域展现出强大的应用潜力。集成了一套完善的工具箱支持多种聚类与分类算法的具体实现。能够显著提升工作效率并为复杂的数据分析任务提供可靠的技术支撑。
一、基于密度的DBSCAN算法
在进行调用DBSCAN算法的接口之前，需要使用命令安装依赖库，命令如下：
install.packages("fpc", dependencies = TRUE)
在R语言的fpc包中包含能够执行DBSCAN聚类算法且具备数据可视化的功能
**dbscan**(data, eps, MinPts, scale, method, seeds, showplot, countmode)**data ：**进行聚类的数据（可以是原始数据矩阵，也可以是一个距离矩阵）；
**eps ：**密度(扫描半径)； **
**
MinPts: 最小包含点数；
scale ：是否对data 标准化（T/F ）；
**mehtod ：**三个可选参数如下，
raw ：将data 视为原始数据，并避免计算距离矩阵（保存存储器，也可以是慢）；
dist ：将data 视为距离矩阵（比较快，但内存价格昂贵）；
**hybrid ：**计算部分距离矩阵（适度的内存需求，非常快）；
seeds:T/F ；
showplot: 是否生成聚类可视化图（三个可选参数值：0表示不生成；1表示每次迭代生成一次；2表示每次子迭代生成一次）
countmode:NULL 或者一个用于报告进度的向量。
样例代码如下：
<span style="font-family:Courier New;font-size:12px;">new1 <- c(0,5183.328938,11420.98223,21320.32421,16989.59236,14899.47468,18480.556186,10386.55199,9236.277226,10180.589785)
new2 <- c(5183.328938,0,12360.82514,22350.72344,16893.23695,20657.25945,11074.88822,11074.88822,9924.613457,9591.926128)
new3 <- c(11420.98223,12360.82514,0,2090.117679,21019.15289,21105.79131,12360.82514,12360.82514,12360.82514,11031.75103)
new4 <- c(21320.32421,22350.72344,2090.117679,0,21019.15289,21105.79131,12360.82514,12360.82514,13603.98286,12071.69154)
new5 <- c(16989.59236,16893.23695,21019.15289,21019.15289,0,5183.328938,17945.32085,15775.28119,20562.67213,20268.02825)
new6 <- c(14899.47468,20657.25945,21105.79131,21105.79131,5183.328938,0,21674.62059,21674.62059,16989.59236,16694.94848)
new7 <- c(18480.556186,11074.88822,12360.82514,12360.82514,17945.32085,21674.62059,0,5576.559036,11954.7204,13959.63176)
new8 <- c(10386.55199,11074.88822,12360.82514,12360.82514,15775.28119,21674.62059,5576.559036,0,11954.7204,13959.63176)
new9 <- c(9236.277226,9924.613457,12360.82514,13603.98286,20562.67213,16989.59236,11954.7204,11954.7204,0,6782.135558)
new10 <- c(10180.589785,9591.926128,11031.75103,12071.69154,20268.02825,16694.94848,13959.63176,13959.63176,6782.135558,0)
X <- rbind(new1,new2,new3,new4,new5,new6,new7,new8,new9,new10)
#X <- scale(X) #标准化
X #距离矩阵
Y <- as.dist(X)
#Y
par(bg="white")
model <- dbscan(X,MinPts=2,eps=7000,scale=F,showplot=2,method="dist")
model
plot(model,X,main="DBSCAN聚类结果",ylab="",xlab="")
</span>
二、层次聚类（hierarchicalclustering）
R语言中包含了名为hcluster(data, method)的函数用于执行层次聚类方法，并不深入探讨该函数的具体参数设置
样例代码如下：
new1 <- c(0,5183.328938,11420.98223,11420.98223,16989.59236,14899.47468,8480.556186,10386.55199,9236.277226,8180.589785)
new2 <- c(5183.328938,0,12360.82514,12360.82514,16893.23695,20657.25945,11074.88822,11074.88822,9924.613457,9591.926128)
new3 <- c(11420.98223,12360.82514,0,2090.117679,21019.15289,21105.79131,12360.82514,12360.82514,12360.82514,11031.75103)
new4 <- c(11420.98223,12360.82514,2090.117679,0,21019.15289,21105.79131,12360.82514,12360.82514,13603.98286,12071.69154)
new5 <- c(16989.59236,16893.23695,21019.15289,21019.15289,0,5183.328938,17945.32085,15775.28119,20562.67213,20268.02825)
new6 <- c(14899.47468,20657.25945,21105.79131,21105.79131,5183.328938,0,21674.62059,21674.62059,16989.59236,16694.94848)
new7 <- c(8480.556186,11074.88822,12360.82514,12360.82514,17945.32085,21674.62059,0,5576.559036,11954.7204,13959.63176)
new8 <- c(10386.55199,11074.88822,12360.82514,12360.82514,15775.28119,21674.62059,5576.559036,0,11954.7204,13959.63176)
new9 <- c(9236.277226,9924.613457,12360.82514,13603.98286,20562.67213,16989.59236,11954.7204,11954.7204,0,6782.135558)
new10 <- c(8180.589785,9591.926128,11031.75103,12071.69154,20268.02825,16694.94848,13959.63176,13959.63176,6782.135558,0)
X <- rbind(new1,new2,new3,new4,new5,new6,new7,new8,new9,new10)
Y <- as.dist(X)
Y
out.hclust <- hclust(Y,"single") #最短距离法
cbind(hc1$merge,hc1$height)
rownames(S)=paste("new",1:10,"")
plclust(out.hclust,sub="",xlab="",ylab="",main="层次聚类结果图") #对结果画图
#rect.hclust(out.hclust,k=5) #用矩形画出分为5类的区域
out.id=cutree(out.hclust,k=5) #得到分为5类的数值
out.id**
**
更多细节：
https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Density-Based_Clustering
https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Hierarchical_Clustering