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)

1
vote
1answer
9 views

Two-stage cluster sampling problem

I'm having difficulty understanding the following problem. It seems like it is a two stage cluster sampling problem, but then the defective red lightbulbs make the question even more difficult. I'd ...
0
votes
0answers
21 views

Analysing ranked data

I had following question in my questionnaire: Rank following factors: price, quality, advertisement, brand, reference from 1 (very important) to 5 (least important) that influenced on your buying ...
0
votes
0answers
7 views

which distance should be used with UPGMA clustering

I am trying to cluster a biological population on the basis of morphological characters using UPGMA clustering method, but I am not sure which distance should I use- Mahalanobis or Euclidean. What are ...
0
votes
1answer
14 views

Cluster analysis for multi-response question

Let's say I have check-all-that-apply survey question. What kind of analysis can I run to understand if there are meaningful clusters (i.e. there's a cluster of people who choose A, B, C, and another ...
1
vote
0answers
17 views

How can you cluster a set of functions with unknown functional forms?

Say you've $N$ functions $f_N(x)$ defined on a regular grid $x$. You don't know the form of $f(x)$, you've only got several realizations of it. The different functions are related to each other ...
0
votes
0answers
12 views

Clustering with Restricted Boltzmann Machine

I am working with the basic RBM that can be found on Geoffrey Hinton's webseite and the MNIST dataset. What I want to do is graphically cluster the input data. I am working with a three layer network ...
0
votes
1answer
172 views

Gaussian neighborhood function and non linear learning rate for self-organizing map in R

I've been working on SOMs and how to get the best clustering results. One approach could be to try many runs and choose the clustering with the lowest within sum of squared errors. However, I do not ...
2
votes
1answer
64 views

Time Series Data Mining Library?

Can anyone recommend a library for time series data mining tasks other than predictive modeling and statistical analysis? There seem to be a number for these purposes (e.g., Gretl), but nothing for ...
0
votes
1answer
104 views

Document image analysis and retrieval with online incremental clustering

Is there any interesting problem in the area of "Document Image Analysis and Retrieval" which by nature needs an online/incremental clustering process ? The problem may be in the context of "Logical ...
0
votes
1answer
184 views

Outlier detection using clustering and dissimilarity matrix in R

I have some problems in finding the outliers using clustering. The data.frame is ~20000 observations and each row has mixed types of variables(numeric, nominal and binary). What I want to do is to ...
0
votes
0answers
26 views

Feature selection for transductive learning

I have a large data set with 30% labelled samples and 70% unlabelled samples. Each sample is a 60-dim vector. My idea is to apply transductive learning to try to label as much as samples as possible, ...
2
votes
1answer
504 views

Cluster analysis on weighted survey data with continuous and categorical variables

I am trying to perform cluster analysis on survey data where each respondent has answered several questions, some of which have categorical answers ("blue" "pink" "green" etc) and some of which have ...
0
votes
0answers
11 views

Grouping search queries by similarity of search results

we run some studies using google search queries. We need to cluster these queries in topics and we would like to find some unsupervised approach. for each query we have the search results. I ...
1
vote
1answer
23 views

How can I calculate cosine distance with multiple feature vectors and weigh them?

I have a dataset of text documents and I'm calculating pairwise cosine distances among them. For each document I have a bag of words vector, a vector built from entities extracted from the document, ...
-1
votes
2answers
52 views

What are Clustering techniques for this case? [duplicate]

What type of clustering methods are available for ordinal, nominal and ratio variables? Suppose I have one ordinal, one nominal and one ratio variable; is there a common clustering technique that can ...
0
votes
0answers
26 views

find group of rows in a matrix with pattern similar to another matrix

I've been scratching my head about this problem for some time: I have a big gene expression dataset (20k genes x 200 samples) in matrix A and i have a subset of this dataset (i.e. 40 genes x 200 ...
0
votes
1answer
325 views

Multidimensional scaling using Python

I have 6,000 points for which I have all pairwise distances in a distance matrix. I want to get an idea whether these data were generated by a mixture of Gaussian distributions so I'm trying to get a ...
6
votes
3answers
773 views

Choosing clusters for k-means: the 1 cluster case

Does anyone know a good method to determine if clustering using kmeans is even appropriate? That is, what if your sample is actually homogenous? I know something like a mixture model (via mclust in R) ...
4
votes
2answers
222 views

Estimating most important features in a k-means cluster partition

Is there a way to determine which features/variables of the dataset are the most important/dominant within a kmeans cluster solution generated via R?
0
votes
0answers
11 views

Dirichlet process mixture model in Python

My question is concerned with the practical issues of using this model. I've tried to use Dirichlet process mixture model from Scikit learn python package to find a number of clusters in my data (1D ...
0
votes
0answers
36 views

How to represent outliers for multi dimensional data (local outlier factor)

Below graph taken from http://en.wikipedia.org/wiki/Local_outlier_factor displays "LOF scores : LOF image : This is great for two dimensional data but what about data > than two dimensions. How ...
1
vote
1answer
47 views

How to evaluate a clustering/unsupervised learning problem with massive amounts of data, with labels only for a small fraction of points

I'm wondering if anybody can point me to work on the evaluation of unsupervised learning where there are a very large (say hundreds of millions) number of points and manual labelling can only ever be ...
4
votes
1answer
618 views

Validate cluster analysis in R

I am trying to validate hierarchical cluster analysis result following a paper by Guy Brock, et al. clValid: An R Package for Cluster Validation (pdf). Do I have to use all these methods? What are the ...
1
vote
3answers
433 views

Rand index calculation

I'm trying to figure out how to calculate the Rand Index of a cluster algorithm, but I'm stuck at the point how to calculate the true and false negatives. At the moment I'm using the example from the ...
1
vote
1answer
58 views

K-means cluster Analysis and 4-point Likert Scales

Is there a concern using a 4-point likert-type scale (i.e., agreement) when attempting a cluster analysis using k-means clustering? Most of the data for the items in my data set are favorable (e.g., ...
1
vote
1answer
66 views

Evaluation measures of overlapping clustering

I have a dataset of Facebook users and a set of different clustering algorithms. The project goal is to draw up a rank between these algorithms in order to understand which of them are the good ones. ...
0
votes
1answer
18 views

How can one evaluate Incremental Clustering Algorithms, in particular the goodness of the clusters formed?

I have been studying an incremental clustering algorithm for a large set of data that exhibit an inherent dynamic behavior (that is new data can get added over time and some older data may get deleted ...
4
votes
1answer
345 views

What is the intuition behind the variation of information (VI) metric for cluster validation?

For non-statisticians like me, it is very difficult to capture the idea of VI metric (variation of information) even after reading the relevant paper by Marina ...
3
votes
2answers
289 views

Clustering of points based on vector feature similarities in R

I have as an input a number of points that I need to partition into clusters. Each point has a number of features that are ideally to be used to find the similarity between each point and the others. ...
2
votes
2answers
92 views

Comparison of close data sets

I'm studying around 100 sets of temperature ($N_{sample}=500$), which depends $4$ explicative variables such as power or speed. The dependency is always the same in each set, but sometimes the mean ...
0
votes
1answer
17 views

Generating even-sized clusters in scikit-learn [duplicate]

I'm attempting to generate approximately even-sized clusters of a PCA'd feature set in Scikit-learn, but I'm not having any luck. I'm only familiar with KMeans clustering, and with that algorithm the ...
0
votes
2answers
89 views

k-means vs k-median?

I know there is k-means clustering algorithm and k-median. One that uses the mean as the center of the cluster and the other uses the median. My question is: when/where to use which?
0
votes
0answers
23 views

Statistical significance of cluster validity

Hi I m working on a unsupervised problem to partition my dataset. I have access to the class labels for this dataset. Now I am trying to use Jaccard coefficient to compute correlation between cluster ...
9
votes
3answers
2k 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 ...
2
votes
1answer
68 views

Detecting strong currents in a sparse directed graph

I have a very large, sparse, weighted, directed graph. The structure is such that it mainly consists of strings of nodes connected with highly weighted edges. These strings can be connected by weak ...
1
vote
0answers
23 views

Spatial cluster analysis

Let's say I have a structure like this : This is a spatial region with measurement of plant population in each site. Black and red represent two regions with different intensities.The question is ...
0
votes
1answer
101 views

Validation of clustering results through correlation maps

How can I compare correlation maps independently to the number of clusters in terms of measuring the 'quality' of well separating (uncorrelated) clusters, i.e. a criterion to maximize the ...
0
votes
1answer
25 views

Cluster migration visualization

I have asked a very similar question at the Latex forum here, but in order to address the part of my question where I ask if there is a better way of visualizing the data I have, I wanted to cross ...
0
votes
1answer
78 views

Interpretation of NbClust result

The plots show the output of NbClust(). By looking at the plot, is that correct to say that k=5 is the optimal number of ...
0
votes
2answers
27 views

Kmeans cluster size change quite a bit on each run

I am running a kmeans on a sample size of 1000 data. The data is scaled (z). When I run kmeans(df, nstart=25, centers=5)- it runs and I can get the size of each cluster. The largest group has 620 in ...
2
votes
0answers
26 views

Heteroskedasticity in a Linear Mixed Model SAS PROC MIXED

Asked a version of this question before but realized it needed some clarification. I have a dataset with identical twin pairs and fraternal twin pairs. I want to examine the relationship between an ...
4
votes
1answer
185 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 ...
0
votes
0answers
17 views

Cluster analysis (proximities)

I have a question regarding clustering. I have a symmetric matrix of 50 specialties (50 X 50) where each cell represents the number of observations related to each combination of specialties. Some ...
1
vote
0answers
17 views

Kmeans plotting on discriminant components

When you plot a kmeans model (in R) with the plotcluster() function, it plots the clusters against the axis of the 1st and 2nd discriminant components (dc). In reading about these axis- some state ...
0
votes
0answers
24 views

How to do clustering using genetic algorithm?

I am studying how to use genetic algorithm (GA) in clustering analysis on R programming. What I understand now is that we have to determine fitness function in GA. That is, we have to minimize within ...
5
votes
1answer
233 views

Spatial clustering with the constraint that all clusters have equal count

I wish to perform a spatial clustering of scattered data that represents geographic locations of individuals in an urban area. Hierarchical clustering seems to work well, and I have successfully done ...
0
votes
0answers
34 views

Variable Clustering Analysis

I have a data set that consists of 143 variables (~11000 observations), and I wish to do variable clustering to reduce the dimension. I am using hclustvar function ...
1
vote
4answers
3k views

Why does k -means clustering algorithm use only Euclidean distance metric?

Is there a specific purpose in terms of efficiency or functionality why the k-means algorithm does not use cosine similarity as a distance metric, but can only use the Euclidean norm?
3
votes
1answer
43 views

Good (2d) visualization of a mixture model clustering

I have a specific problem which I'm surprised I don't find answers on-line and I hope somebody here has a good suggestion for me. I'm working with a large data set which I'm clustering into specific ...
0
votes
1answer
28 views

Autoclass in R/Python? [closed]

Are there any packages that implement the Autoclass/ Naive Bayes Clustering algorithm in R or Python? Alternatively, what are some other clustering algorithms that can handle both categorical and ...