I started with a dataset that contained many dimensions for individuals (each id is a separate individual), and extracted three
Features/Attributes columns for each
My goal is to bucket these individuals into two or three distinct groups, based on these features, to see if I can identify distinct separations between certain groups.
I was wondering if anyone has any recommendations, or algorithms (preferably in Python) that would cluster these individuals into distinct groups? I do not have these
Individuals classified, so it's an unsupervised clustering problem. I was thinking that
K-Means might be a good option, or something similar to PCA which could reduce my dimensionality and provide insight into the Features that seem to separate the groups of individuals into distinct groups.
Thanks for looking!
Note: The data shown below was artificially generated to illustrate my question.
Appendix: Reference Data:
Pattern,Feature A,Feature B,Feature C 6,2.18,0.13,8.00 9,9.31,3.67,6.58 11,0.89,1.83,4.33 13,9.73,9.50,1.59 23,0.51,6.49,0.09 26,9.04,3.42,3.90 27,8.35,9.18,3.40 28,3.04,5.63,6.88 32,9.78,6.52,8.50 43,4.11,3.36,1.83 49,9.57,1.52,7.09 51,1.13,9.98,9.42 53,6.22,1.37,7.07 62,8.79,3.03,7.52 63,6.27,7.29,0.98 71,4.64,0.06,6.55 73,1.34,9.32,5.15 83,4.53,3.85,2.04 84,9.48,9.71,3.23 86,3.80,3.00,0.76 88,1.73,0.64,9.96