Tagged Questions

1
vote
0answers
29 views

Using PCA to merge and grade correlated items

I have a real estates' condos sold dataset with the following fields DOM: Date on the market sellPct: Percentage difference between the original and final price. other fields such as Exposure( ...
0
votes
0answers
37 views

What are “factorial coordinates”?

The concept of "factorial coordinates" seems to arise in PCA and clustering contexts, from what I've gleaned from web searches, but I can't find a definition. I'm interested in repeating an analysis ...
2
votes
4answers
97 views

Grouping samples by clustering or PCA

If I have 5 binary variables with values for 100 observations to give me a 5x100 matrix. ...
0
votes
1answer
91 views

To rotate or not to rotate post-PCA and pre-cluster analysis

Questions in respect to rotation post-PCA have been answered before -> its all in the hands of the researcher... Same answer to the question if rotation (orthogonal or not) makes sense before plugging ...
1
vote
0answers
36 views

Comparing ballot data from two consecutive elections

I have two datasets of general elections (in a multi-party single-vote system) which I'm trying to analyze. Each has about 10,000 data points (ballots) with some 30+ features - the main features are ...
1
vote
1answer
97 views

Reducing no of variables subsetted based on depth for PCA

First of all, sorry for the strange title, I had no idea how to describe my problem better. My issue is the following, I think it is pretty much limited to geosciences. I have several properties for ...
1
vote
2answers
114 views

In non-negative matrix factorization, does the first N eigenvector have N greatest variance?

I know for PCA, it's true that the first N eigenvectors have N greatest variance. But I'm not sure whether that's also true for NMF(Non-negative Matrix Factorization). For example, this ...
3
votes
2answers
196 views

Classification on principal components

For my research I am doing classification on the dataset of three variables. I run unsupervised clustering (based on a histogram peak technique of cluster analysis)and the result I evaluated visually ...
1
vote
3answers
100 views

How can I separate each of 100 observations into groups as determined by the data?

I have 3 covariates for 100 observations. How can I separate each of my 100 observations into groups as determined by the data. I was thinking clustering. However, apparently, I need more than 3 ...
4
votes
2answers
104 views

What are features that distinguish clustering, blind signal separation and dimensionality reduction?

In terms of input -> [process] -> output what are features that distinguish clustering, blind signal separation and dimensionality reduction? From this ...
4
votes
2answers
284 views

Appropriateness of PCA to visualize clusters in genetic data

I've seen PCA improperly applied in genetic research quite often. I wanted to clarify : when is it appropriate to use PCA as a visualization tool in your analysis? Some examples: 1) Rarely is the % ...
4
votes
4answers
1k views

Can I use principal component analysis loadings to reduce number of variables for inclusion in a cluster analysis?

I have to reduce the number of variables to conduct a cluster analysis. My variables are strongly correlated, so I thought to do a Factor Analysis. However, if I use the resulting scores of factor ...
7
votes
5answers
261 views

Dimensionality reduction technique to maximize separation of known clusters?

So let's say I have a bunch of data points in R^n, where n is pretty big (like, 50). I know this data falls into 3 clusters, and I know which cluster each data point is a part of. All I want to do is ...
0
votes
1answer
237 views

Clustering of multivariate data

Please I am about to cluster some data based which have about 15 different columns all of which are numbers(Some categorical while some are measurements) also some of my values are missing in some ...
7
votes
2answers
363 views

When do we combine dimensionality reduction with clustering?

I am trying to perform document-level clustering. I constructed the term-document frequency matrix and I am trying to cluster these high dimensional vectors using k-means. Instead of directly ...
5
votes
4answers
357 views

How to create one score from a mixed set of positive and negative variables?

I have 3,000 observations (administrative communities) characterized by five variables. Four of them work in the direction 'the more, the worse' and one goes in the opposite. I'd like to create one ...