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August 3rd, 2018, 11:47 AM   #1
Joined: Aug 2018
From: alberta

Posts: 2
Thanks: 0

Exotic stats problem ; mode, fuzzy clusters, etc


Hi I'm working on a math problem and not sure how to approach it.

Trying to enumerate, rank and extract most common numeric ranges
from a list, with a twist ;

Basic operation example is, I have ten numeric values representing
weights in variable unit measure types, i.e. grams, ounces, etc.

Each value is unique and is a decimal value,
for example we have the following set of numbers
shown below. In this set, we are interested in the
most common magnitudinal range. Here the most
common range value is shown by three values ;

295.999, 312.015, 330.111

the complete set is shown ;

What I want to be able to do, is to input a list of ten values similar to the above,
and have some way, to simply, easily derive the most common value by range.

If I was using values that were more static, for example if all value in the range
in the list were all similar such as "310", then I could just use the mode function
and it would easily tell me this.

However since the values are variable decimal types, I am a bit stumped as to how
I would accomplish this. I came across Python Fuzzy Clustering and it looks like
this might work possibly in relation to mode but wondering if there is a simpler, easier, faster
way to do this?

The end goal is I want to be able to do pattern analysis on a list of numbers and return
the most common range of highest magnitude.

Outputs desired, as an example from the list above would be the thee values printed
to screen, text file, or variable

???? better way to do this than ;

1.) Fuzzy Clustering
2.) Mode (most common value) of discrete data.

What do I mean by define "most common value by range"

Any set of ten values, there will be a few values that reoccur, but with slight variance. So for the above the most common range value would be 300 but with minor variance for each instance. What I need to do is to have a function that can tell what number magnitude occurs the most even though each instance is not fixed. If I had a set of ten numbers and out of ten I had three exact values occur I could just use a mode function, but since here - the magnitudes are comparable but not exact, mode won't work in the standard case, but there must be a way to achieve this?

Why am I using "three" common range values?

"three" is stated in the original post 'as an example'. Here, I'm saying we have a fixed data set
of ten decimal numeric values. In the set, a certain quantity of the ten values will approach a similar threshold of magnitude, the other values with be random. The threshold 'cluster magnitude' could be any range, in a spectral type set. So here I chose three values, only to illustrate the concept I am after.

This could change, so out of ten there could be four, five, six values with similar magnitudes.

Another way to view this problem might be like a sensor array, where each of the ten values in the set are picking up a pulse value represented by a decimal number and we want to interpret similar values across the set as something like pressure, a curve, etc.

The point is, while we can use a python mode type function to extract most common numeric values from a list where the values are exactly alike and fixed, here we cannot use mode, because the values are not exactly alike, only approximately alike, such as the examples I gave of ; 295.999, 312.015, 330.111

Last edited by skipjack; August 3rd, 2018 at 06:22 PM.
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