0
$\begingroup$

Social scientist here with little background in stats. I have a question regarding a PCA I've carried out on my data. I have 17 variables catching different properties of neighbourhoods (geometrical and topological). The correlation matrix shows little correlation among them; as I understand, that's okay because it means my variables are actually catching different features of the individuals (300 neighbourhoods). I ran PCA using FactoMineR, I generated the factor map, and then cut the dendrogram using HCPC. The dendrogram comes out with a line that cuts the it at 5, which, I asume is the optimal number of clusters. Here's my questions:

1) The factor map indicates that my two first principal components explain 48% of the variance in the data. I've read that this might be due to that the correlation among variables is not very significant. Is this really important? My goal is mostly to group the individuals by type and claim that there's x types of neighbourhoods in my city.

2) The dendrogram seems to indicate 5 as an optimal number of clusters but when I cut it at 8 it seems closer to what I'm looking for. Would it be statistically wrong if I decided to go for 8 and omit the grouping of 5? I know that 8 sounds arbitrary and that I maybe should have gone with a simple k-means clustering but I tried that and it didn't work. The groups k-means generated didn't make any sense.

Any help greatly appreciated.

JF

enter image description here

enter image description here

$\endgroup$
0
$\begingroup$

If the first two components would explain almost 100%, then this would essentially say that most of your attributes are meaningless. So no, you do not want this value to be too high.

At the same time, if you would need all 17 components, you will likely already see the curse of dimensionality where similarity becomes hard to decide.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.