Tagged Questions

0
votes
2answers
52 views

Non-distance metrics in hierarchical clustering? [closed]

What happens, intuitively, when one uses non-distance metrics to calculate the distance matrix that feeds into a standard hierarchical clustering algorithm? What mistakes will the algorithm make and ...
2
votes
2answers
386 views

Numerical Instability of calculating inverse covariance matrix

I have a 65 samples of 21-dimensional data (pasted here) and I am constructing the covariance matrix from it. When computed in C++ I get the covariance matrix pasted here. And when computed in matlab ...
3
votes
1answer
120 views

What are the use cases related to cluster analysis of different distance metrics?

I'm trying to use different distance metrics like Euclidean, Manhattan, cosine, chebyshev among other distance metrics in my k-means algorithm to calculate distances between the data points and the ...
5
votes
2answers
188 views

Calculating similarity and clustering question

I have a dataset of about a million companies containing their names, total employees and annual sales. I want to come up with a function that when given the company returns the 5 most similar ...
2
votes
2answers
129 views

What function of distance for the questionnaire data?

I have data from questionnaire from school. First question is study program (only 2 programs) and next 35 questions are various questions (influence of friends etc.) Possible answers for 35 questions ...
0
votes
2answers
208 views

Interpret Silhouette plot for large microarray dataset

For a microarray experiment with ~40,000 probes and ~30 samples I used the clara function from R to cluster my expression matrix. How do I interpret this silhouette plot? Firstly, I don't ...
1
vote
3answers
131 views

Clustering algorithm and distance function for sets

I am willing to run a clustering algorithm on data records consisting in sets each one representing the features enabled at a certain time. Is there any clustering algorithm you would recommend me to ...
2
votes
2answers
160 views

(hierarchical) cluster analysis with non-standard distance

My question is triggered by a question that was asked on stackoverflow: http://stackoverflow.com/questions/12198115/using-different-metric-for-hclust-linkage. The thing is this: I can formulate an ...
2
votes
1answer
606 views

Euclidean Distance b/t unit vectors or cosine similarity where vectors are document vectors

I was reading Similarity Measures and suddenly my whole world was falling apart. I have implemented a search engine using clustering techniques. For clustering, I used k means which uses Euclidean ...
0
votes
3answers
705 views

Using a cosine similarity does not work for any dataset

I have a clustering algorithm, where if I use an euclidian distance as similarity, it works well on any dataset. If I replace it by a cosine similarity (see my code bellow), it will give a degenerate ...
0
votes
1answer
100 views

How can I replace this condition by a probability?

I want to see if a datapoint x should (or not) be assigned to a nearest component y using the following condition: if ($d > T$) then {do not assign x to y}. With $d = distance(x,y)$ and $T = ...
0
votes
1answer
116 views

Assigning new observations into existing clusters made from a distance matrix

I am building a classification model (binary outcome) and would like to include an external cluster membership code as a predictor. For training the model, this is straight forward. When "scoring" new ...
0
votes
2answers
562 views

How to input self-defined distance function in R?

I want to know how to to input a self-defined distance in R, in hierarchical clustering analysis. R implements only some default distance metrics, for example "Euclidean", "Manhattan" etc. Suppose I ...
1
vote
1answer
464 views

Entropy based on euclidian distances between datapoints / clusters centers?

Is it possible to define any useful entropy or conditional entropy which is based on the distance between datapoint(s) and cluster center(s), instead of basing on the number of points assigned to ...
4
votes
5answers
198 views

Significance of difference using a distance matrix

So my data consists of a distance matrix for some points, and a table classifying the points as either red or green. I want to know if there is any difference between the red and green points. I did ...
2
votes
2answers
361 views

How to derive a distance function based on multiple variables for cluster analysis?

I am not a statistician, so please excuse my lack of statistics knowledge/terminology. I have bunch of network nodes that I want to run cluster analysis on and identify clusters. So as far as I ...
4
votes
2answers
394 views

Will the silhouette formula change depending on the distance metric?

I am using Silhouette width to compute the best value for k in k-means. As I am performing document clustering, I am calculating the values of a and ...
11
votes
2answers
301 views

$L_1$ or $L_.5$ metrics for clustering?

Does anyone use the $L_1$ or $L_.5$ metrics for clustering, rather than $L_2$ ? Aggarwal et al., On the surprising behavior of distance metrics in high dimensional space said (in 2001) that $L_1$ ...
9
votes
3answers
1k views

Is it ok to use Manhattan distance with Ward's inter-cluster linkage in hierarchical clustering?

I am using hierarchical clustering to analyse time series data. My code is implemented using the Mathematica function DirectAgglomerate[...], which generates ...
6
votes
1answer
342 views

Derivation of distance in two-step clustering

I am working with the two step cluster process in SPSS Modeler (Clementine) and trying to get a sense for the distance function used. It is a log-likelihood function (as stated in docs) but I am ...
7
votes
1answer
2k views

Clustering: Should I use the Jensen-Shannon Divergence or its square?

I am clustering probability distributions using the Affinity Propagation algorithm, and I plan to use Jensen-Shannon Divergence as my distance metric. Is it correct to use JSD itself as the distance, ...