K means clustering inadequate in determining extreme regions in R 
I want to identify the regions that are considerably higher than the highest cluster. (The obvious regions which should be identified as their own clusters, notably at the x coordinate ~10 e+07. How would I be able to identify that using a clustering algorithm?
I am using R algorithm kmeans in the picture above. with 6 centers:
kmeans(numbers_vector, centers=6, nstart=10)
What can I do to alleviate the inadequacy of this algorithm? Use a different clustering algorithm? Have more centers? But If I have more centers, it identifies many more regions in the center (namely clusters 3,5, and 6). Any ideas? 
Here is histogram and density. It is important to note that the histogram and density plot DO NOT show the spike ~x=10e+07, because the number of points involved in the spike ~20, perhaps are completely overshadowed by the ~107,350 points plotted.


Data: x (88289 obs.);   Bandwidth 'bw' = 0.7574
   x                 y            

Min.   : -1.272   Min.   :0.000e+00
 1st Qu.: 40.114   1st Qu.:4.110e-06
 Median : 81.500   Median :3.167e-05
 Mean   : 81.500   Mean   :6.035e-03
 3rd Qu.:122.886   3rd Qu.:2.886e-03
 Max.   :164.272   Max.   :4.827e-02  
 A: K-means will partition to minimize variance.
The minimum variance parition does produce these slices.
Instead of using clustering, use density estimation. You have already plotted densities for your data. It's fairly obvious how to identify low density regions in the histogram (if you don't want to use bins, you can use kernel density estimation). The low values will also be separated from the majority of the data by a low density region.
The spike you are interested in is an area with really low density.
I.e. split your data set at a density minimum, and select data in areas with low density.
A: How would I be able to identify that using a clustering algorithm?
Assuming you want to identity outliers or where the groups break at (your question isn't exactly clear to me), I recommend plotting your data and then plotting your centers.  This will plot your data points color coded by cluster and then plot the cluster centers over it.
c1<-kmeans(numbers_vector, centers=6, nstart=10)
plot(numbers_vector, col = cl1$cluster)
    points(cl1$centers, col = 11, pch = 8, cex = 2,lwd=5)

Without actual data, I can't be much more help.
I am using R algorithm kmeans in the picture above. with 6 centers:
The picture above appears not to have shown through.  Can you provide your dataset?
What can I do to alleviate the inadequacy of this algorithm? Use a different clustering algorithm? Have more centers? But If I have more centers, it identifies many more regions in the center (namely clusters 3,5, and 6). Any ideas? 
Again, without data your questions are ambiguous and difficult to answer.
