I'm working with a data set and I am relatively un-versed in statistics / statistical terminology (my background is computer science) though I'm pretty techie and might know a bit more than some.
I'm working with a data set where I have a number of integer values (ranging from 1-100) and am counting the number of times each value occurs. These values tend to cluster over the total scale and have an average error of about .75 (rounding, basically) so they tend to fall into two consecutive (or sometimes three) buckets rather than a single one.
Here's an example of the data:
6 occurred 159 times in set - 33.9019 percent 5 occurred 133 times in set - 28.3582 percent 25 occurred 72 times in set - 15.3518 percent 24 occurred 69 times in set - 14.7122 percent 78 occurred 13 times in set - 2.77185 percent 76 occurred 13 times in set - 2.77185 percent 95 occurred 5 times in set - 1.0661 percent 97 occurred 4 times in set - 0.852879 percent
For example, in the set above both 5 / 6 are basically the same number (bucket) for my use and are very important due to the fact that they make up approx. 60% of the values. Similarly 24/25 are important with 30% overall. If there were a 23 or 26 it would also mix into that 30% bucket.
Values with less than 2-3% registration are basically noise so while the 76 / 78 are related (and probably useful) they're likely to be read as noise and thrown out which is ok but not ideal.
What I'm interested in is identifying the clusters over the overall range most efficiently and which ones contain the greatest number of samples.
Right now I'm using a brute force method of sorting by "value" and then adding buckets together which happen to be consecutive but I'm convinced there's probably some statistical method that will serve to "find and identify clusters"
Any leads / recommendations greatly appreciated!