Repeat the clustering process only using the Rep house votes dataset - What differences and similarities did you see between how the clustering worked for the datasets?

## $cluster
##   [1] 1 1 1 1 2 2 2 1 2 1 2 2 2 2 1 2 1 1 2 2 2 2 1 1 2 1 2 1 2 1 1 1 1 2 2 1 2
##  [38] 2 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 2 2 1 2 1 1 1 1 1 2 2 2 2 1 1 2 1 1 1 1
##  [75] 1 2 2 1 2 2 2 1 2 1 2 2 1 1 1 1 2 2 2 1 1 1 2 1 1 1 1 2 2 1 2 1 2 2 1 2 2
## [112] 2 1 1 1 2 2 1 2 2 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 2 1 2 2 1 2 1 1 1 1 1 2 2
## [149] 1 1 2 2 2 1 2 1 2 1 2 1 2 2 1 2 1 2 1 2 2 2 1 1 2 1 1 1 1 2 2 2 2 1 2 1 1
## [186] 2 2 2 2 1 2 1 2 1 1 1 2 2 1 1 2 1 1 1 2 1 2 2 2 1 2 2 2 2 1 1 2 2 1 2 1 2
## [223] 1 1 1 2 2 2 2 2 2 1 1 2 2 2 1 1 2 1 1 1 1 1 2 2 2 2 1 1 1 1 2 2 1 1 1 2 2
## [260] 2 1 2 1 1 1 2 1 2 1 2 2 2 2 1 2 2 2 1 1 1 1 1 1 1 2 2 1 1 2 2 2 2 2 1 1 1
## [297] 1 2 1 2 2 2 2 1 2 1 1 2 1 1 2 2 2 1 1 2 1 2 2 1 2 2 1 1 2 2 1 1 2 2 2 1 2
## [334] 1 2 2 2 1 1 1 2 2 2 2 2 2 2 1 2 2 1 1 1 2 2 2 1 2 2 2 2 1 2 1 2 2 1 1 1 1
## [371] 2 2 2 1 2 1 1 1 2 1 2 2 1 2 2 1 2 1 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2 2 1 2
## [408] 2 1 1 2 1 2 2 2 2 2 1 2 1 2 1 2 1 1 2 1
## 
## $centers
##         aye      nay    other
## 1  84.00481 130.7548 98.24038
## 2 126.15982 114.1872 72.65297
## 
## $totss
## [1] 401339.9
## 
## $withinss
## [1] 73355.47 39284.36
## 
## $tot.withinss
## [1] 112639.8
## 
## $betweenss
## [1] 288700

## [1] 0.7193405

## *** : The Hubert index is a graphical method of determining the number of clusters.
##                 In the plot of Hubert index, we seek a significant knee that corresponds to a 
##                 significant increase of the value of the measure i.e the significant peak in Hubert
##                 index second differences plot. 
## 

## *** : The D index is a graphical method of determining the number of clusters. 
##                 In the plot of D index, we seek a significant knee (the significant peak in Dindex
##                 second differences plot) that corresponds to a significant increase of the value of
##                 the measure. 
##  
## ******************************************************************* 
## * Among all indices:                                                
## * 14 proposed 2 as the best number of clusters 
## * 3 proposed 3 as the best number of clusters 
## * 1 proposed 4 as the best number of clusters 
## * 3 proposed 6 as the best number of clusters 
## * 1 proposed 9 as the best number of clusters 
## * 1 proposed 10 as the best number of clusters 
## * 1 proposed 15 as the best number of clusters 
## 
##                    ***** Conclusion *****                            
##  
## * According to the majority rule, the best number of clusters is  2 
##  
##  
## *******************************************************************
## null device 
##           1

## [1] 0.7193405
## [1] 0.795011

Conclusions: From our second clustering procedure, we see that largely the results from the democratic introduced bills are very comparable to those of the repbulican introduced bills. The data is well separated, but not perfectly separated. This is good if you are the role of a lobbyist. This means that opposing parties can, and will, vote the opposite of their party for particular bills.

In a separate Rmarkdown document work through a similar process with the NBA data.

You are a scout for the worst team in the NBA, probably the Wizards. Your general manager just heard about Data Science and thinks it can solve all the teams problems!!! She wants you to figure out a way to find players that are high performing but maybe not highly paid that you can steal to get your team out of the toilet!

Details:

Hints: