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.
85
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 ...
25
votes
3answers
3k 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 ...
22
votes
5answers
4k 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 ...
21
votes
3answers
2k 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 ...
21
votes
6answers
6k 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 ...
21
votes
4answers
1k 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 ...
20
votes
5answers
2k 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, ...
20
votes
2answers
577 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 ...
17
votes
7answers
4k 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 ...
16
votes
8answers
2k 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 ...
15
votes
4answers
2k 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
5answers
1k 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 ...
13
votes
8answers
1k 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 ...
13
votes
1answer
3k 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?
11
votes
4answers
3k 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 ...
11
votes
2answers
2k 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).
...
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
2k 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 ...
9
votes
5answers
295 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 ...
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 ...
9
votes
1answer
430 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
1answer
946 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 ...
9
votes
2answers
139 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 ...
8
votes
7answers
1k views
How to perform k-means clustering with only a distance function, not euclidean points?
I want to perform k-means clusteirng 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 ...
8
votes
4answers
264 views
How to tell quantitatively whether 1D data is clustered around 1 or 3 values?
I've got some data on the time between heart beats of a human. One indication of ectopic (extra) beats is that these intervals are clustered around three values instead of one. How can I obtain a ...
8
votes
3answers
455 views
How to cluster longitudinal variables?
I have a bunch of variables which contain longitudinal data from day 0 to day 7. I am looking for an appropriate clustering approach which can cluster these longitudinal variables (not cases) into ...
8
votes
3answers
1k 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 ...
8
votes
5answers
2k views
Clustering genes in a time course experiment
I have seen a few queries on clustering in time series and specifically on clustering, but I don't think they answer my question.
Background: I want to cluster genes in a time course experiment in ...
8
votes
3answers
144 views
Detecting clusters in a binary sequence
I have a binary sequence such as 11111011011110101100000000000100101011011111101111100000000000011010100000010000000011101111
Where clusters of mostly 1's are ...
8
votes
3answers
149 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 ...
8
votes
1answer
684 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 ...
8
votes
2answers
821 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 ...
8
votes
1answer
144 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 ...
7
votes
7answers
947 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 ...
7
votes
3answers
1k views
How can I test whether my clustering of binary data is significant
I'm doing shopping cart analyses my dataset is set of transaction vectors, with the items the products being bought.
When applying k-means on the transactions, I will always get some result. A random ...
7
votes
2answers
517 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 ...
7
votes
4answers
523 views
Clustering of a matrix (homogeneity measurement)
I have a 2 dim matrix, and I want to know e.g. all the higher values are in the upper left corner. I can't just project it into R^3 and use a standard clustering algorithm because I don't want to ...
7
votes
8answers
895 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 ...
7
votes
5answers
261 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 ...
7
votes
3answers
478 views
Mixture Models and Dirichlet Process Mixtures (beginner lectures or papers)
In the context of online clustering, I often find many papers talking about: "dirichlet process" and "finite/infinite mixture models".
Given that I've never used or read about dirichlet process or ...
7
votes
3answers
138 views
Mining search logs to improve autocomplete suggestions?
I have logs from an autocomplete form, which I would like to leverage to increase the intelligence of the results it returns.
I have a project that revolves around users selecting opera characters ...
7
votes
2answers
769 views
How to find groupings (trajectories) among longitudinal data?
Context
I want to set the scene before somewhat expanding on the question.
I have longitudinal data, measurements taken on subjects approximately every 3 months, primary outcome is numeric (as in ...
7
votes
3answers
355 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 ...
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, ...
7
votes
2answers
362 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 ...
7
votes
5answers
238 views
Does preclustering help to build a better predictive model?
For the task of churn modelling I was considering:
Compute k clusters for the data
Build k models for each cluster individually.
The rationale for that is,that there is nothing to prove, that the ...
7
votes
2answers
206 views
Clustering with asymmetrical distance measures
How do you cluster a feature with an asymmetrical distance measure?
For example, let's say you are clustering a dataset with days of the week as a feature - the distance from Monday to Friday is not ...
7
votes
1answer
845 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:
...
7
votes
1answer
170 views
Merging spatial and temporal clusters
I've items that have a geo-spatial position and a temporal origin. For both dimensions, I build clusters so far.
I'm now in search of a way to merge this different clusters forming spatio-temporal ...
7
votes
2answers
700 views
Density-based spatial clustering of applications with noise (DBSCAN) clustering in R
this question started as "Clustering spatial data in R" and now has moved to DBSCAN question.
As the responses to the first question suggested I searched information about DBSCAN and read some docs ...
