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
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