# How can I cluster a continuous percentage? [closed]

I've got a table with 3 columns. 1 is descriptive A 1 is a percentage B 1 is a decimal number C

I would like to investigate for the clusters (ie. B is high ad C is low, or viceversa). How can I start this task?

I'm not very experienced with this task in Mathematica. I've used Excel to plot it but I think is really not satisfying.

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## closed as off-topic by Oleksandr R., Karsten 7., bbgodfrey, Kuba, m_goldbergMar 2 '15 at 13:47

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You could start by showing what you've tried, and providing some example data and example results you're after (the latter c/b fabricated, just to show readers what you want). – ciao Mar 1 '15 at 22:58
I've added an image and some analysis. But I'm not very used to cluster analysis with mathematica. I've never used it for this purpose before.. You can see the clusters very clearly in the graph.. – Revious Mar 1 '15 at 23:04
Off-topic because not enough information is given to proceed in a useful way, plus the question seems to be more about how to do something productive with the data in general, than how to do it in Mathematica. – Oleksandr R. Mar 2 '15 at 12:20
@OleksandrR.: mmm.. your point is pretty interesting. The creator of this community (StackExchange) said that the environment leads and change the people which are living in it. When you split the environment (Mathematica) from the solution of Data Analysis provided, you are making a non trivial ipothesi. The opinion you just expressed (the question seems to be more about how to do something productive with the data in general, than how to do it in Mathematica) is more an intuition or an exhaustive analysis that you would sign with your name, as a professionist? – Revious Mar 2 '15 at 13:45
It is stated as a personal opinion that other people may agree or disagee with at their option. StackExchange sites are community-driven and based on a voting system for most decisions; judging by the votes, at least four other people agreed with me. Simply put, your question is not of the standard we are accustomed to receiving on this site, and if you want to have it re-opened, you should take care to clarify it. To turn your objection around, would you be willing to sign this poor effort with your real name, I wonder? Also, please note that it was not me who downvoted you. – Oleksandr R. Mar 2 '15 at 14:32

The data provided is missing the descriptor, which could be also used for data analysis. Using the data provided above.

data = {{19, 5.5}, {20, 30}, {25, 3.9}, {38, 4}, {43, 30}, {10,
88}, {12, 6.4}, {67, 13}, {67, 23}, {100, 10}, {3, 9.9}, {3,
5.9}, {12, 21}, {12, 5.1}, {17, 20}, {20, 24}, {25, 16}, {33,
24}, {33, 40}, {50, 5}, {50, 3.5}, {50, 8}, {100, 10}, {100,
22}, {100, 35}, {100, 1}, {100, 18}, {100, 10}, {100, 32}, {100,
44}, {100, 1}, {100, 23}, {100, 55}, {100, 5}, {0, 40}, {0,
19}, {0, 8.6}, {0, 31}, {0, 5.9}, {0, 41}, {0, 8.8}, {0,
33}, {0, 39}, {0, 11}, {0, 10}, {0, 67}, {0, 3.3}, {0, 38}, {0,
150}, {0, 23}, {0, 260}, {0, 7.9}, {0, 70}, {0, 230}, {0,
7.1}, {0, 120}, {0, 260}, {0, 30}, {0, 7.1}, {0, 23}, {0,
7.5}, {0, 20}, {0, 10}, {0, 27}, {0, 24}, {0, 1}, {0, 8.6}, {0,
22}, {0, 32}, {0, 4.2}, {0, 28}, {0, 32}, {0, 5.3}, {0, 29}, {0,
37}, {0, 38}, {0, 11}, {0, 6}, {0, 21}, {0, 13}, {0, 1.8}, {0,
140}, {0, 19}, {0, 7.2}, {0, 25}, {0, 7}, {0, 8}, {0, 37}, {0,
5.5}, {0, 20}, {0, 88}, {0, 38}, {0, 45}, {0, 10}, {0, 29}, {0,
34}, {0, 44}, {0, 9.7}, {0, 8.3}, {0, 20}, {0, 38}, {0, 35}, {0,
190}, {0, 7}, {0, 9}, {0, 41}, {0, 5.3}, {0, 29}, {0, 10}, {0,
41}, {0, 11}, {0, 20}, {0, 26}, {0, 4}, {0, 28}, {0, 5}, {0,
5.5}, {0, 47}, {0, 11}, {0, 17}, {0, 5}, {0, 30}, {0, 20}, {0,
210}, {0, 2.5}, {0, 10}, {0, 44}, {0, 25}, {0, 30}, {0, 34}, {0,
31}, {0, 4}, {0, 57}, {0, 45}, {0, 21}, {0, 42}, {0, 170}, {0,
10}, {0, 43}, {0, 18}, {0, 2}, {0, 6}, {0, 4}, {0, 7.5}, {0,
21}, {0, 95}, {0, 8.5}, {0, 26}, {0, 94}, {0, 4}, {0, 8}, {0,
90}, {0, 9}, {0, 44}, {0, 110}, {0, 3}, {0, 49}, {0, 17}, {0,
87}, {0, 62}, {0, 37}, {0, 27}, {0, 29}, {0, 160}, {0, 30}, {0,
98}, {0, 6}, {0, 20}, {0, 4}, {0, 35}, {0, 33}, {0, 230}, {0,
3}, {0, 21}, {0, 4}, {0, 10}, {0, 16}, {0, 7}, {0, 49}, {0,
450}, {0, 4}, {0, 27}, {0, 4}, {0, 44}, {0, 20}, {0, 15}, {0,
160}, {0, 20}, {0, 36}, {0, 5}, {0, 10}, {0, 1}, {0, 10}, {0,
20}, {0, 21}, {0, 4}, {0, 5}, {0, 80}, {0, 27}, {0, 37}, {0,
19}, {0, 130}, {0, 12}, {0, 4}, {0, 72}, {0, 10}, {0, 35}, {0,
34}, {0, 48}, {0, 49}, {0, 24}, {0, 46}, {0, 27}, {0, 6}, {0,
30}, {0, 43}, {0, 8}, {0, 59}, {0, 120}, {0, 31}, {0, 23}, {0,
16}, {0, 96}, {0, 23}, {0, 49}, {0, 69}, {0, 38}, {0, 25}, {0,
44}, {0, 41}, {0, 28}, {0, 110}, {0, 6}, {0, 8}, {0, 3}, {0,
42}, {0, 14}, {0, 330}, {0, 120}, {0, 63}, {0, 21}, {0, 21}, {0,
9}, {0, 8}, {0, 74}, {0, 42}, {0, 6}, {0, 12}, {0, 7}, {0,
48}, {0, 12}, {0, 83}, {0, 210}, {0, 34}, {0, 350}, {0, 32}, {0,
5}, {0, 18}, {0, 6}, {0, 82}, {0, 27}, {0, 190}, {0, 400}, {0,
29}, {0, 19}, {0, 100}, {0, 5}, {0, 10}, {0, 13}, {0, 40}, {0,
33}, {0, 15}, {0, 39}, {0, 6}, {0, 7}, {0, 7}, {0, 140}, {0,
64}, {0, 450}, {0, 48}, {0, 63}, {0, 53}, {0, 45}, {0, 49}, {0,
10}, {0, 10}, {0, 36}, {0, 43}, {0, 14}, {0, 21}, {0, 3}, {0,
38}, {0, 230}, {0, 160}, {0, 23}, {0, 250}, {0, 44}, {0, 24}, {0,
42}, {0, 13}, {0, 81}, {0, 5}, {0, 46}, {0, 18}, {0, 32}, {0,
110}, {0, 49}, {0, 3}};
Histogram3D[data]


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Thanks. Are you also familiar with the function: FindClusters? I would like to keep the description while making clusters. I am going to edit the question and add it. – Revious Mar 2 '15 at 11:32