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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96
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 ...
91
votes
2answers
10k views

How to understand the drawbacks of K-means

K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a data set and a pre-specified number of clusters, k, then I just ...
90
votes
6answers
10k views

Why is Euclidean distance not a good metric in high dimensions?

I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high ...
36
votes
6answers
10k 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, ...
36
votes
3answers
10k 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 ...
34
votes
5answers
14k 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 ...
32
votes
6answers
6k views

Euclidean distance is usually not good for sparse data?

I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data ...
30
votes
3answers
6k 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 ...
28
votes
6answers
11k 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 ...
24
votes
7answers
10k 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 ...
24
votes
4answers
4k 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 ...
23
votes
2answers
749 views

Detecting patterns of cheating on a multi-question exam

QUESTION: I have binary data on exam questions (correct/incorrect). Some individuals might have had prior access to a subset of questions and their correct answers. I don’t know who, how many, or ...
22
votes
8answers
12k views

How to decide on the correct number of clusters?

We find the cluster centers and assign points into different bins in k-means clustering which is a very simple algorithm and is found almost in every machine learning material on the net. But the ...
21
votes
3answers
5k 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 ...
19
votes
5answers
11k 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 ...
17
votes
3answers
13k 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 ...
17
votes
1answer
8k 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?
15
votes
7answers
4k 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 ...
15
votes
4answers
4k views

Assumptions of cluster analysis

Apologies for the rudimentary question, I am new to this form of analysis and have a very limited understanding of the principles so far. I was just wondering if many of the parametric assumptions ...
14
votes
2answers
694 views

Why does gap statistic for k-means suggest one cluster, even though there are obviously two of them?

I am using K-means to cluster my data and was looking for a way to suggest an "optimal" cluster number. Gap statistics seems to be a common way to find a good cluster number. For some reason it ...
14
votes
8answers
4k 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 ...
14
votes
2answers
3k views

Nonparametric Bayesian analysis in R

I am looking for a good tutorial on clustering data in R using hierarchical dirichlet process (HDP) (one of the recent and popular nonparametric Bayesian methods). ...
13
votes
4answers
7k views

Clustering a dataset with both discrete and continuous variables

I have a dataset X which has 10 dimensions, 4 of which are discrete values. In fact, those 4 discrete variables are ordinal, i.e. a higher value implies a higher/better semantic. 2 of these discrete ...
13
votes
3answers
450 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 $L_1$ ...
12
votes
4answers
480 views

How to measure shape of cluster?

I know that this question is not well defined, but some clusters tend to be elliptical or lie in lower dimensional space whilst the other have nonlinear shapes (in 2D or 3D examples). Is there any ...
11
votes
8answers
3k views

What are the “hot algorithms” for machine learning?

This is a naive question from someone starting to learn machine learning. I'm reading these days the book "Machine Learning: An algorithmic perspective" from Marsland. I find it useful as an ...
11
votes
3answers
6k views

Reason to normalize in euclidean distance measures in hierarchical clustering

Apparently, in hierarchical clustering in which the distance measure is Euclidean distance, the data must be first normalized or standardized to prevent the covariate with the highest variance from ...
11
votes
2answers
7k views

Are mean normalization and feature scaling needed for k-means clustering?

What are the best (recommended) pre-processing steps before performing k-means?
11
votes
1answer
5k 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 ...
11
votes
1answer
4k views

Clustering variables based on correlations between them

Questions: I have a large correlation matrix. Instead of clustering individual correlations, I want to cluster variables based on their correlations to each other, ie if variable A and variable B ...
10
votes
8answers
2k views

Clustering quality measure

I have a clustering algorithm (not k-means) with input parameter $k$ (number of clusters). After performing clustering I'd like to get some quantitative measure of quality of this clustering. The ...
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 ...
10
votes
4answers
1k views

Appropriate clustering techniques for temporal data?

I have temporal data of activity frequencies. I want to identify clusters in the data that indicate distinct periods of time with similar activity levels. Ideally I want to identify the clusters ...
10
votes
1answer
722 views

Detect circular patterns in point cloud data

For some volume reconstruction algorithm I'm working on, I need to detect an arbitrary number of circular patterns in 3d point data (coming from a LIDAR device). The patterns can be arbitrarily ...
9
votes
2answers
3k 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 ...
9
votes
4answers
538 views

Are there cases where there is no optimal k in k-means?

This has been inside my mind for at least a few hours. I was trying to find an optimal k for the output from the k-means algorithm (with a cosine similarity metric) so I ended up plotting the ...
9
votes
3answers
4k views

Understanding comparisons of clustering results

I'm experimenting with classifying data into groups. I'm quite new to this topic, and trying to understand the output of some of the analysis. Using examples from Quick-R, several ...
9
votes
5answers
2k views

Recommended books or articles as introduction to Cluster Analysis?

I'm working on a small (200M) corpus of text, which I want to explore with some cluster analysis. What books or articles on that subject would you recommend?
9
votes
3answers
3k 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 ...
9
votes
4answers
3k views

Clustering procedure where each cluster has an equal number of points?

I have some points $X=\{x_1,...,x_n\}$ in $R^p$, and I want to cluster the points so that: Each cluster contains an equal number of elements of $X$. (Assume that the number of clusters divides $n$.) ...
9
votes
3answers
256 views

Space-efficient clustering

Most clustering algorithms I've seen start with creating a each-to-each distances among all points, which becomes problematic on larger datasets. Is there one that doesn't do it? Or does it in some ...
9
votes
3answers
2k views

Clustering (k-means, or otherwise) with a minimum cluster size constraint

I need to cluster units into $k$ clusters to minimize within-group sum of squares (WSS), but I need to ensure that the clusters each contain at least $m$ units. Any idea if any of R's clustering ...
9
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 ...
9
votes
2answers
165 views

Detecting Clusters of “similar” source codes

Assume I have 400 students (that's in a big university) that have to do a computer science project, and that they have to work alone (no group of students). An example of project could be let ...
9
votes
2answers
283 views

SOM clustering for nominal/circular variables

Just wondering if anyone is familiar with clustering nominal inputs. I've been looking at SOM as a solution but apparently it only works with numerical features. Are there any extensions for ...
9
votes
2answers
2k views

Clustering spatial data in R

I have a set of sea surface temperature (SST) monthly data and I want to apply some cluster methodology to detect regions with similar SST patterns. I have a set of monthly data files running from ...
9
votes
2answers
361 views

Finding a known number of circle centers that maximize the number of points within a fixed distance

I have a set of 2-D data where I want to find the centers of a specified number of centers of circles ($N$) that maximize the total number of points within a specified distance ($R$). e.g. I have ...
9
votes
0answers
707 views

Clustered standard errors vs. multilevel modeling?

I've skimmed through several books (Raudenbush & Bryk, Snijders & Bosker, Gelman & Hill, etc.) and several articles (Gelman, Jusko, Primo & Jacobsmeier, etc.), and I still haven't ...
8
votes
4answers
438 views

Are there any non-distance based clustering algorithms?

It seems that for K-means and other related algorithms, clustering is based off calculating distance between points. Is there one that works without it? Thanks!
8
votes
6answers
12k views

Categorical variables clustering with R

I wonder whether it is possible to perform within R a clustering of mixed data variables. In other words I have a data set containing both numerical and categorical variables within and I'm finding ...