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.

learn more… | top users | synonyms (1)

10
votes
1answer
763 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 ...
5
votes
4answers
529 views

How to create one score from a mixed set of positive and negative variables?

I have 3,000 observations (administrative communities) characterized by five variables. Four of them work in the direction 'the more, the worse' and one goes in the opposite. I'd like to create one ...
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 ...
2
votes
1answer
2k views

How can I compare Likert scale data of two clusters in SPSS?

In SPSS, I want to compare two clusters of management sciences department faculty members in two universities. Which test should I use? Can you explain how to do it in SPSS?
23
votes
2answers
772 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 ...
3
votes
1answer
114 views

Max-margin clustering with size constraint

Given a dataset $D$ and a distance measure, I want to split the dataset into two disjoint subsets $X, Y$ of a specified size (say 80% and 20% of the original size), so that the minimum distance of all ...
6
votes
1answer
582 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 ...
6
votes
3answers
973 views

How to generate user-friendly summaries of cluster analysis?

I used BIRCH and HAC to do clustering on my data. I want to now what type of information I can include in reports that my users can generate to get more insights on the clusters. I would have to dumb ...
9
votes
1answer
4k 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, ...
5
votes
1answer
773 views

Sequential clustering algorithm

I want to cluster elements in array. The crucial difference from a normal clustering algorithm is that the order of elements is significant. For instance if we look at a simple sequence of numbers ...
7
votes
2answers
877 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 ...
8
votes
1answer
6k views

Using the stats package in R for kmeans clustering

I'm having difficulty understanding one or two aspects of the cluster package. I'm following the example from Quick-R closely, but don't understand one or two aspects of the analysis. I've included ...
97
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 ...
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 ...
5
votes
3answers
361 views

Semantic distance between excerpts of text

I'm wondering how far along the natural language processing is in determining the semantic distance between two excerpts of text. For instance, consider the following phrases Early today, I got up ...
5
votes
3answers
5k 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?
6
votes
2answers
940 views

Choice of weight function in Moran's I

I'm doing an autocorrelation analysis for a spatially distributed collection of observations. To perform my analysis, I am using Moran's I statistic. My questions are: (1) What are the implications ...
4
votes
1answer
202 views

Multiple hypothesis ANOVA

I'm not sure that I've titled this question correctly, but here is my query. Suppose you are given a set of measurements and the uncertainty (variance) associated with each. The task is to ...
7
votes
1answer
238 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 ...
4
votes
0answers
77 views

In which case does FCM membership converge to 1/K?

I have tested the fuzzy C-means (FCM) algorithm using the R function fanny from the cluster package and I have wrote my own FCM ...
3
votes
2answers
609 views

R package for symbolic data analysis

Most methods for symbolic data analyis are currently implemented in the SODAS software. Are there any R packages for symbolic data except clamix and clusterSim?
10
votes
8answers
3k 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 ...
6
votes
1answer
815 views

Cluster data points by distance between clusters

I have a load of point data in 3D, and I wish to cluster them like so: Each cluster contains points all of which are at most distance $d$ from another point in the cluster. All points in two ...
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 ...
1
vote
1answer
151 views

Are there any conditions on the data in Fuzzy c-mean clustering?

I want to cluster a graph using the FCM algorithm, I used the adjacency matrix of the graph as the data, and the "Euclidean" distance as metric. The problem is that the adjacency matrix is full of ...
5
votes
0answers
278 views

Recommended method for finding archetypes or clusters

I wish to cluster users together in a database, with each user represented by a number of features that are both discrete and continuous in nature. The aim is to define a small number of archetypal ...
2
votes
1answer
202 views

What type of statistical analysis solves this problem?

I have database of 78706 resident incidents in aged care facilities (5 years of data). I want to to learn and implement a tool allowing analyzing these data using following attributes: Resident ...
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). ...
6
votes
1answer
741 views

Use coefficients of thin plate regression splines in a clustering method

There are numerous procedures for functional data clustering based on orthonormal basis functions. I have a series of models built with the GAMM models, using the ...
7
votes
3answers
3k views

How do you test an implementation of k-means?

Disclaimer: I posted this question on Stackoverflow, but I thought maybe this is better suited for this platform. How do you test your own k-means implementation for multidimensional data sets? I ...
2
votes
1answer
535 views

How to do weighted pair hierarchical clustering in R?

Here is an example of hierarchical clustering of genes in the microarray data using the weighted pair gene method in Spotfire. I am not sure how to do this in ...
4
votes
4answers
596 views

Analyze and generate “clumpy” distributions?

Are there standard ways of analyzing and generating "clumpy" distributions? analyze: how clumpy is a given point cloud (in 1d, 2d, nd), what are its clumpy coefficients? generate or synthesize a ...
38
votes
3answers
11k 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
15k 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 ...
5
votes
1answer
402 views

A measure to describe the distribution of a dendrogram

Could anyone suggest some statistical measures to describe the distribution of a dendrogram? If I have two dendrograms, how could can I quantify their structural differences?
9
votes
1answer
2k 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 ...
5
votes
5answers
311 views

Patient distance metrics

There are umpteen million research papers regarding relationships between various patient attributes (e.g. how does gene x affect condition y?). What I am interested in though is a distance metric ...
4
votes
1answer
263 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 ...
30
votes
3answers
7k 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 ...
8
votes
3answers
3k 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 ...
28
votes
6answers
12k 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 ...
7
votes
4answers
840 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 ...
24
votes
4answers
5k 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 ...
6
votes
1answer
2k views

Interpreting output of igraph's fastgreedy.community clustering method

With the help of several people in this community I have been wetting my feet in clustering some social network data using igraph's implementation of modularity-based clustering. I am having some ...
13
votes
1answer
5k 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 ...
25
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 ...
4
votes
4answers
1k views

Algorithm for choosing the number of clusters when using pam in R?

I am clustering a dataset using the pam command (from {cluster} package), and I wish to decide on the number of clusters to use. I was able to implement The_Elbow_Method in R (see wiki) for doing ...
22
votes
5answers
12k 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 ...
7
votes
1answer
739 views

Can someone explain the C-Index in the context of hierarchical clustering?

This is a followup to this question. I am currently trying to implement the C-Index in order to find a near-optimal number of clusters from a hierarchy of clusters. I do this by calculating 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 ...