0
votes
0answers
19 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 ...
1
vote
2answers
88 views

Relationship between dimentionality reduction and clustering algorithms

I've got bit confused about dimensionality reduction and clustering . whether all clustering algorithms (k-means, affinity propagation, spectral clustering,...) do kind of dimensionality reduction ?
1
vote
0answers
38 views

What is exactly code vector and quantization vector of self organizing map?

I am trying to understand code vector in self organizing map. Could anybody explain me intuitively what it is exactly?
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 ...
0
votes
1answer
103 views

Noise in clustering of high dimensional sparse data

Questions: 1) How to detect noise variables in high dimensional data? 2) Does the method that is presented below make sense? 3) What clustering methods are most insensitive to random variables in ...
1
vote
2answers
181 views

Evaluation of k-means output for >3D

I'm implementing the k-means algorithm (in R Map-Reduce) and I wanted to verify if the output I'm getting is close enough to the true centroids of the cluster. This is how I'm verifying with a 2D ...
3
votes
2answers
415 views

Dimension reduction for sparse matrix for clustering

I'm looking for a Sparse matrix dimension reduction. I already used some feature selection methods like PCA but it doesn't give me good results. I want to apply mixture models for clustering my data. ...
1
vote
2answers
192 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 ...
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
189 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
598 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 % ...
3
votes
1answer
206 views

How to decide if to do dimensionality reduction before clustering?

Is there any agreement on when to reduce data dimension before clustering in order to avoid curse of dimensionality? My intuition is that if I have say 1000 points and data dimension is 10 then it is ...
2
votes
3answers
395 views

How to cluster survey data?

I have designed a rather long (250 Qn) survey designed to uncover user clusters. The questions are such that the pattern of answering should elicit user clusters, but I am having trouble uncovering ...
4
votes
3answers
593 views

Dimensionality reduction using self-organizing map

Self-organizing maps are claimed to be an approach for dimensionality reduction. However, I am kind of confused about this claim. Consider the following example, I have a data set with 200 data ...
4
votes
1answer
498 views

Suggestions for multi-dimensional clustering

I am working in a genomics project and I ended up having a huge table with around 800 measurements (cases/rows), around 200 channels (columns/continuous variables) and 5 categories (one categorical ...
7
votes
5answers
463 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 ...
21
votes
4answers
3k views

How to do dimensionality reduction in R

I have a matrix where a(i,j) tells me how many times individual i viewed page j. There are 27K individuals and 95K pages. I would like to have a handful of "dimensions" or "aspects" in the space of ...