Partitioning data into subsets of objects according to their mutual "similarity," without using preexisting knowledge such as class labels. Clustered-standard-errors and/or cluster-samples should be tagged as such; do not use the "clustering" tag for them.

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27
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5answers
5k views

How to tell if data is “clustered” enough for clustering algorithms to produce meaningful results?

How would you know if your (high dimensional) data exhibits enough clustering so that results from kmeans or other clustering algorithm is actually meaningful? For k-means algorithm in particular, ...
5
votes
1answer
294 views

How can I group numerical data into naturally forming “brackets”? (e.g. income)

The following describes what I'm trying to accomplish, but it's possible an alternative problem statement can describe my goal: I want to divide the following numbers into groups where the ...
4
votes
4answers
6k views

k-means implementation with custom distance matrix in input

Can anyone point me out a k-means implementation (it would be better if in matlab) that can take the distance matrix in input? The standard matlab implementation needs the observation matrix in input ...
25
votes
3answers
4k views

Is it possible to do time-series clustering based on curve shape?

I have sales data for a series of outlets, and want to categorise them based on the shape of their curves over time. The data looks roughly like this (but obviously isn't random, and has some missing ...
6
votes
1answer
1k views

Hierarchical clustering with mixed type data - what distance/similarity to use?

In my dataset we have both continuous and naturally discrete variables. I want to know whether we can do hierarchical clustering using both type of variables. And if yes, what distance measure is ...
14
votes
4answers
6k views

Clustering with a distance matrix

I have a (symmetric) matrix M that represents the distance between each pair of nodes. For example, A B C D E F G H I J K L A 0 20 ...
27
votes
5answers
10k views

Where to cut a dendrogram?

Hierarchical clustering can be represented by a dendrogram. Cutting a dendrogram at a certain level gives a set of clusters. Cutting at another level gives another set of clusters. How would you pick ...
8
votes
3answers
2k views

Comparing clusterings: Rand Index vs Variation of Information

I was wondering if anybody had any insight or intuition behind the difference between the Variation of Information and the Rand Index for comparing clusterings. I have read the paper "Comparing ...
18
votes
3answers
3k views

What stop-criteria for agglomerative hierarchical clustering are used in practice?

I have found extensive literature proposing all sorts of criteria (e.g. Glenn et al. 1985(pdf) and Jung et al. 2002(pdf)). However, most of these are not that easy to implement (at least from my ...
24
votes
5answers
8k views

Time series 'clustering' in R

I have a set of time series data. Each series covers the same period, although the actual dates in each time series may not all 'line up' exactly. That is to say, if the Time series were to be read ...
33
votes
3answers
6k views

Choosing clustering method

When using cluster analysis on a data set to group similar cases, one needs to choose among a large number of clustering methods and measures of distance. Sometimes, one choice might influence the ...
13
votes
8answers
2k views

Visualization software for clustering

I want to cluster ~22000 points. Many clustering algorithms work better with higher quality initial guesses. What tools exist that can give me a good idea of the rough shape of the data? I do want to ...
15
votes
3answers
6k views

How to interpret mean of Silhouette plot?

Im trying to use silhouette plot to determine the number of cluster in my dataset. Given the dataset Train , i used the following matlab code ...
8
votes
1answer
3k 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, ...
4
votes
1answer
2k views

Two-stage clustering in R

Is it possible to do 2-stage cluster analysis in R? Can anybody provide me resource on it?
4
votes
2answers
802 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 ...
21
votes
7answers
7k views

How to do community detection in a weighted social network/graph?

I'm wondering if someone could suggest what are good starting points when it comes to performing community detection/graph partitioning/clustering on a graph that has weighted, undirected edges. The ...
14
votes
1answer
6k views

How to define number of clusters in K-means clustering?

Is there any way to determine the optimal cluster number or should I just try different values and check the error rates to decide on the best value?
8
votes
1answer
1k views

Time series clustering

I have many time series in this format 1 column in which I have date (d/m/yr) format and many columns that represent different time series like here: ...
5
votes
1answer
3k views

Elbow criteria to determine number of cluster

It is mentioned in wiki page that one of the method to determine the optimal number of cluster in the dataset is "elbow method". Here the percentage of variance is calculated as the ratio of the ...
5
votes
1answer
373 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 ...
6
votes
2answers
2k views

Determine different clusters of 1d data from database

I have a database table of data transfers between different nodes. This is a huge database (with nearly 40 million transfers). One of the attributes is the number of bytes (nbytes) transfers which ...
4
votes
3answers
627 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 ...
2
votes
2answers
824 views

How to determine the number of clusters when using correlation as the distance?

How does using 1 - correlation as the distance influence the determination of the number of clusters when doing kmeans? Is it still valid to use the classical indices (Dunn, Davies-Bouldin...)?
89
votes
8answers
13k views

Detecting a given face in a database of facial images

I'm working on a little project involving the faces of twitter users via their profile pictures. A problem I've encountered is that after I filter out all but the images that are clear portrait ...
13
votes
7answers
2k views

How to perform k-means clustering with only a distance function, not euclidean points?

I want to perform k-means clustering on some objects I have, but the objects aren't described by "points". However, I am able to compute the distance between any two objects (it is based on a ...
10
votes
1answer
3k views

What is an acceptable value of the Calinski & Harabasz (CH) criterion?

I have done a data analysis trying to cluster longitudinal data using R and the kml package. My data contains of around 400 individual trajectories (as it is called in the paper). You can see my ...
6
votes
4answers
482 views

Working through a clustering problem

Say I've got a program that monitors a news feed and as I'm monitoring it I'd like to discover when a bunch of stories come out with a particular keyword in the title. Ideally I want to know when ...
13
votes
3answers
402 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 $L_1$ ...
7
votes
2answers
694 views

Getting started with biclustering

I have been doing some casual internet research on biclusters. (I have read the Wiki article several times.) So far, it seems as if there are few definitions or standard terminology. I was ...
5
votes
2answers
258 views

Does a distance have to be a “metric” for an hierarchical clustering to be valid on it?

Let us say that we define a distance, which is not a metric, between N items. Based on this distance we then use an Agglomerative hierarchical clustering. Can we use each of the known algorithm ...
3
votes
3answers
596 views

Univariate clustering of time series

I just want to know if its possible to cluster an univariate time series, in order , say, to detect anomalies? and do you have any online version for denstream code, in Matlab? here is the time ...
10
votes
3answers
2k 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
3answers
786 views

Clustering distributions

I have several distributions (10 distributions in the figure below). In fact these are histograms: there are 70 values on the x-axis which are the sizes of some particles in a solution and for each ...
5
votes
3answers
838 views

Evaluation measure of clustering (without having truth labels)

I'm clustering a set of data but I don't have truth document that allow me to evaluate the result of clustering (I have unlabelled data), so I can not use an external evaluation measure. In this case, ...
4
votes
1answer
2k views

What to do when sample covariance matrix is not invertible?

I am working on some clustering techniques, where for a given cluster of d-dimension vectors I assume a multivariate normal distribution and calculate the sample d-dimensional mean vector and the ...
4
votes
1answer
236 views

Smoothness of a surface

I am currently working on a model which takes two parameters and produces a measurement statistic. Think of it as Z = f(X,Y). Z is a matrix of my statistics and I am creating a surface plot of it in ...
3
votes
1answer
153 views

Self organizing maps vs. kernel k-means

For an application, I want to cluster data (potentially high dimensional) and extract probability of belonging to a cluster. I consider at the moment Self organizing maps or kernel k-means to do the ...
3
votes
3answers
2k views

Classification after factor analysis

I have analysed several dimensions in a survey. Each part of the survey represents a theoretical dimension and is analysed with factorial analysis. I want to use scores from factor analysis to do a ...
2
votes
2answers
365 views

How to determine which method is the most reasonable clustering results?

Method 1: Cluster by K-means with initial centroid {27, 67.5} Method 2: Cluster by K-means with initial centroid {22.5, 60} Method3: Agglomerative Clustering How can I know which method ...
3
votes
1answer
651 views

Measurement of similarity for hierarchical clustering trees

I am performing a number of hierarchical clusters on a dataframe of patient records (e.g. similar to http://www.biomedcentral.com/1471-2105/5/126/figure/F1?highres=y) I am experimenting with ...
3
votes
1answer
1k views

K-means & BIC (to validate clusters) in R

I'm wondering if there is a good way to calculate the clustering criterion based on BIC formula, for a k-means output in R? I'm a bit confused as to how to calculate that BIC so that I can compare it ...
3
votes
1answer
2k views

Gower's dissimilarity index

I would like to ask a question about Gower similarity/dissimilarity index. Is it ok to use the Gower dissimilarity measure with Ward linkage clustering? I was reading that the Gower similarity index ...
2
votes
2answers
993 views

Why don't dummy variables have the continuous adjacent category problem in cluster analysis?

I know that if we use categorical variables in cluster analysis we would assume that the scale is continuous and we don't have this concept of distance between two adjacent categories. But what is the ...
0
votes
1answer
324 views

Incremental or online or single pass or data stream clustering refers to the same thing?

Incremental clustering algorithms Online clustering algorithms Data stream clustering algorithms Single pass clustering algorithms Are the following expressions related? Does some of them include ...
8
votes
1answer
1k views

Cluster Analysis followed by Discriminant Analysis

What is the rationale, if any, to use Discriminant Analysis (DA) on the results of a clustering algorithm like k-means, as I see it from time to time in the literature (essentially on clinical ...
4
votes
3answers
3k views

Plotting a heatmap given a dendrogram and a distance matrix in R

I have dendrogram and a distance matrix. I wish to compute a heatmap -- without re-doing the distance matrix and clustering. Is there a function in R that permits this?
3
votes
2answers
708 views

Ways to measure distance from multivariate Gaussian (Mahalanobis distance)

I have a cluster of p-dimensional points and given a new p-dimensional point $x$ I want to determine whether or not it is likely to belong to this cluster. The cluster is made up of $n$ ...
2
votes
0answers
536 views

Using Davies-Bouldin index in clustering

I am clustering data using k-medoid. I used Davies–Bouldin index for $2$ to $n-1$ clusters. Here $n = 100$ (using smaller test case). I find minimal value of the index for 98 clusters. But the overall ...
1
vote
5answers
178 views

Kolmogorov-Smirnov test - reliability

Description I want to use Kolmogorov-Smirnov test to check how given clusters of 1D points differs from normal distribution (original question here: How to test which data match model at best). I am ...