0
votes
1answer
17 views

Generating even-sized clusters in scikit-learn [duplicate]

I'm attempting to generate approximately even-sized clusters of a PCA'd feature set in Scikit-learn, but I'm not having any luck. I'm only familiar with KMeans clustering, and with that algorithm the ...
0
votes
0answers
52 views

PCA scores and cluster analysis

I have obtained PCA scores (Anderson-Rubin) and thought to use them in cluster analysis, but have got confused how to make interpretations based on them or as what type of variable they should be ...
0
votes
0answers
21 views

Appropriate PCA/ EFA rotation method

Question about appropriate PCA/ EFA rotation method. While I see that oblique rotation methods (e.g. promax) are suggested for correlated data (and I have correlated data) I see that the majority of ...
1
vote
1answer
28 views

Binomial data and PCA and cluster analysis

I have obtained responses on around 48 items measuring employer attractiveness. The goal of my analysis is to cluster respondents according to these employer attractiveness dimensions. I plan to ...
1
vote
1answer
117 views

Pull out most important variables from PCA

I would like to get the most important variables from a PCA result. I see two clusters in the plot. I now that is possible that there is no only one variable causing this, so maybe I would have to get ...
2
votes
4answers
257 views

Clustering binary categorical data

I have some data where I have certain classes (c1, c2, c3, c4 ...) and the data comprises of binary vectors where 1 and 0 denote that an entry belongs to a class or not. The number of classes will be ...
3
votes
1answer
197 views

Using PC scores or cluster analsis in predictions

I have very big data and low number of observations. So I decided to use PCA to reduce dimension of the data. The following is R example (just an dummy example - for workout): ...
0
votes
0answers
20 views

Subspace clustering with random transformation

One approach for clustering a high dimensional dataset is to use linear transformation, and the most common approaches are PCA and random projection (where random projection arises from the ...
0
votes
3answers
76 views

Problem with PCA

I am trying to do some PC analysis on my data coming from lipids measurements in different samples. I only have one factor: if samples are diabetic or non-dibetic. Here is the PCA graph I get: As ...
1
vote
1answer
100 views

cluster plot: working and interpretation ?

Recently I have come across usage of cluster plot, which combines k-mean clustering along with PCA. The plot shows different clusters plotted using first two PCs. I have checked some of the threads ...
2
votes
0answers
38 views

Cluster analysis on related factors

I am analyzing a public data set of information security incident data and trying to find "clusters" of related factors. Specifically, each incident is analyzed using VERIS for the actor's variety ...
0
votes
0answers
22 views

processing a sound file for analysis of different spoken languages

So i have sound files for 5 languages by 2 person, thus my input data has 10 sound files. Now i want to cluster them based on the languages (thus 5 clusters) and not based on the speaker/voice ( ...
0
votes
1answer
63 views

Can component scores be used for further analyses, e.g. cluster analysis?

I have done a principal component analysis using SPSS and now have 3 components. 2 components have 4 items in the subscale, and 1 component has 3 items. Component scores using regression for each ...
2
votes
1answer
53 views

Multivariate data analyis of compositional data

Suppose I have a multivariate, compositional dataset that depicts the concentration of different elements. However, the data are not available on a single scale; i.e., some are of form 0.00x while ...
5
votes
1answer
321 views

Interpret clustering plotted in the first two principal components

I got this plot when I plotted a clustering object in R. If I run km <- clara(data, 2), then plot(km), I get a similar ...
0
votes
0answers
107 views

Which variables to retain in order to preserve the same clustering pattern?

Suppose I have 50 scale parameters, these are all genes measured for one sample from a subject at the clinic, after data reduction by PCA, two meaningful components were extracted. This was followed ...
3
votes
1answer
229 views

Follow up of cluster analysis with membership prediction

I have 11 scale parameters for each of 218 observations belonging to subjects, I did standardized PCA to reduce dimensionality of the data and found two meaningful components. Using Euclidean ...
2
votes
1answer
427 views

Interpretation of PCA biplot?

I just ran my first ever PCA, so please excuse any naivety on my part. As input, I used five years worth of the following: S&P/ASX 200 A-REIT S&P/ASX 200 Consumer Discretionary S&P/ASX ...
1
vote
1answer
156 views

Clustering of documents that are very different in number of words

I have a corpus of 643 documents with different sizes and my goal is to cluster them according their topics and label each cluster with semantic name for its main topic. I have tired different ...
3
votes
3answers
302 views

Reducing variables and correlated variables in a dataset

I am attempting to produce models on a dataset with approximately 60 variables and 800 subjects. Other datasets are also being considered for use. The datasets were not originally constructed for this ...
0
votes
1answer
56 views

Reduce dimensions on a data set and its clusters' centroids

I am building a small application to calculate clusters from some input set (a n x p matrix). After I finish running the algorithm to get k clusters I also obtain the centroid of each cluster (a k x p ...
3
votes
2answers
2k views

LSA vs. PCA (document clustering)

I'm investigation various techniques used in document clustering and I would like to clear some doubts concerning PCA (principal component analysis) and LSA (latent semantic analysis). First thing - ...
2
votes
0answers
87 views

Principal Component Analysis (PCA) for binary data [duplicate]

First of all, I would like to note that I have read similar topics in CrossValidated but I am not fully satisfied. I have a dataset which consists of an $N\times M$ binary matrix. 1 means that an ...
1
vote
0answers
45 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( ...
2
votes
4answers
240 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
365 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
64 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
2answers
210 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 ...
4
votes
2answers
429 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
111 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
194 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
640 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 % ...
6
votes
4answers
3k 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
489 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
280 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 ...
8
votes
2answers
744 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
431 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 ...