0
votes
1answer
24 views

Cluster with distance threshold in R

I'd like to get clusters with a maximum inner distance threshold. Now I use hc <- hclust(d) and cutree(hc, numofclasses). ...
1
vote
1answer
53 views

cluster plot: working and interpretation ?

Recently I have come across usage of cluster plot, which combines k-mean clustering along with PCA. The plot shows different clusters plotted using first two PCs. I have checked some of the threads ...
1
vote
0answers
40 views

Clustering time series of measurements in R

I have a dataframe consisting time series of measurements taken every hour for 366 days or a year. Below is shown a sample of hourly measurements for the first two weeks. I want to cluster days with ...
1
vote
0answers
18 views

Finding multivariate clusters with survey data (in R)

I'd like to conduct a multivariate cluster analysis on data from the American Community Survey's PUMS microsample (individual level records). I've only performed cluster analysis before when there are ...
1
vote
0answers
16 views

Clustering Techniques

I'm a little new to data mining and would definitely appreciate some tips. I'm using clustering algorithms looking for possible grouping in some variables described below. I've been using the Excel ...
2
votes
2answers
109 views

Special method for forecasting on time-series clusters in R?

I'm doing a project related to identifying sales dynamics. My database contains 26 weeks after launching the product (so 26 time-series observations equally spaced in time). I used two methods ...
1
vote
0answers
28 views

Advice on how to analyse “customer-data” in R

consider the following example data: ...
2
votes
0answers
19 views

Maximam r distance for Ripley's K-function

I am using R's package spatstat to study the locational pattern of conflict events in Africa (around 8.000 points) using point pattern analysis techniques. I was able to obtain the plot of g(r), the ...
1
vote
1answer
41 views

got stuck in Cluster analysis, way forward?

I have a situation, where I need to classify items into groups (lets say 6). When I ran k-means 90% of my data fall in 1 group remaining 10% fall in other groups. What's next step? In order to further ...
1
vote
0answers
38 views

Relative Variable Importance in Clustering

In SPSS, the user can check the relative variable importance in a clustering result and produce a graph like the following: link: ...
4
votes
2answers
91 views

What's the easiest way to separate two populations in a scatterplot?

I have to separate two populations by a line in a scatterplot: I would like find a threshold that separates the two populations. In @Waynes words, I would like to cluster the points into two ...
1
vote
0answers
31 views

Clustering time-shifted sales time-series

I need to perform clustering and classification of time series of weekly sales of different products. My data are weekly sales of different products in differest areas (stores). The challenges on this ...
0
votes
0answers
35 views

Variance within each cluster

I have done some clustering to a matrix with 30 random variables , each variable has 13000 observations ). i got 10 clusters and now i need to test how good the clustering is by calculating the ...
5
votes
1answer
112 views

Interpret clustering plotted in the first two principal components

I got this plot when I plotted a clustering object in R. If I run km <- clara(data, 2), then plot(km), I get a similar ...
0
votes
2answers
152 views

Clustering a binary matrix

I have a semi-small matrix of binary features of dimension 250k x 100. Each row is a user and the columns are binary "tags" of some user behavior e.g. "likes_cats". ...
0
votes
0answers
42 views

Evaluation of the results of hiererchical clustering

I have used hclust function from R for the hierarchical clustering of vectors which are already labeled. ...
2
votes
0answers
65 views

Clustering 2d data using kernel density methods

Assume I have data looking like this ...
2
votes
1answer
167 views

Clustering algorithms that operate on sparse data matricies [closed]

I'm trying to compile a list of clustering algorithms that are: Implemented in R Operate on sparse data matrices (not (dis)similarity matrices), such as those created by the sparseMatrix function. ...
2
votes
1answer
133 views

Finding the kink in a bivariate relationship

I'm investigating which methods are generally used to dichotomise an ordinal variable Y so that it maximises the between-group differences in the values of X and minimises the within-group differences ...
1
vote
1answer
82 views

Difficulty to intrepret pvclust results - only many low level tree clusters appear as significant

I'm trying to assess the uncertainty in hierarchical cluster analysis. It is a dataset composed of 409 observations and 27 variables (with a value ranging form 0 to 100). The dataset represents ...
0
votes
1answer
76 views

Gaussian neighborhood function and non linear learning rate for SOM 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 ...
1
vote
1answer
109 views

Is there a function in R that takes the centers of clusters that were found and assigns clusters to a new data set

I have two parts of a multidimensional data set, let's call them train and test. And I want to built a model based on the train data set and then validate it on the test data set. The number of ...
0
votes
0answers
99 views

Which variables to retain in order to preserve the same clustering pattern?

Suppose I have 50 scale parameters, these are all genes measured for one sample from a subject at the clinic, after data reduction by PCA, two meaningful components were extracted. This was followed ...
3
votes
1answer
122 views

Follow up of cluster analysis with membership prediction

I have 11 scale parameters for each of 218 observations belonging to subjects, I did standardized PCA to reduce dimensionality of the data and found two meaningful components. Using Euclidean ...
3
votes
1answer
166 views

What is the intuition behind the variation of information (VI) metric against others 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
140 views

How to figure out what numbers often appear together in a dataset?

I have a lottery style dataset we produce internally (example below). I am trying to figure out which numbers appear most frequently together. Example questions: What are the top 10 pair of numbers ...
1
vote
1answer
77 views

Subspace clustering in R using package orclus

Currently I am working on some subspace clustering issues. I found one useful package in R called orclus, which implemented one subspace clustering algorithm called orclus. As stated in the package ...
0
votes
1answer
152 views

Simple way to categorize: terrible, poor, average, good, excellent

I have a data frame with the following: ...
2
votes
0answers
68 views

Why does some model-based clustering fail to fit with a large number of dimensions?

I am attempting to cluster data using Mclust. The data is originally from a dissimilarity matrix, transformed via multidimensional scaling in R (MASS::isoMDS). As I ...
1
vote
1answer
54 views

How do you find weight of edges between individuals based on co-participation in the same groups? (SNA)

I have a data set with 20,000 discussion forum threads, many only one or two posts, some up to 400-posts. I have 5,000 individuals who participated in these threads. I want to calculate the strength ...
1
vote
1answer
74 views

Cluster large boolean dataset

I got a dataset with about 5,000 columns and about 135,000 rows - all fields contain boolean (binary) data. I am looking to classify each of these columns into one of 50 groups, based on similarity. ...
1
vote
0answers
54 views

How to filter only positive correlations on R? [closed]

Is it possible to filter only positive correlations on R? The point is to make clusters of time series using the correlation as a distance measure, but without clustering the series that have ...
1
vote
0answers
97 views

Evaluating clusters of variables produced by varclus & hclustvar

Background - I want to cluster analyze a mixed dataset, clustering the variables on the basis of correlational similarity. SPSS gives me this option, but doesn't allow me to evaluate the clustering ...
0
votes
1answer
76 views

Quantitative cluster evaluation

I have a clustering results by different unsupervised algorithms in the form of coordinates (x,y) and class (it's currently binary). I would like evaluate the quality of clustering by some value. Most ...
4
votes
1answer
301 views

Is it OK to use correlated variables for cluster analysis?

I know there is a series of regression diagnostics procedures (correlation, beta, residual, etc.) before, during, and after regression analysis. But, is there any common procedure to follow for ...
2
votes
2answers
144 views

Suggestions for clustering ordinal, non-normal data (unsupervised)

I have data from about 250 self-injurers and would like to cluster analyze their reported motivations for self injury (and then explore cluster membership vis-a-vis various psychological measures). I ...
1
vote
1answer
188 views

Partitional vs. hierarchical clustering with distance matrix

I am exploring the flexibility of partitional clustering algorithms. In particular, I would like to introduce more general distances than the ones which are used by default. Let us consider, for ...
1
vote
1answer
156 views

What is the initial partition for k-means in R?

My question is probably elementary, and I apologize for that. I am reading Kogan's "Introduction to Clustering Large and High-Dimensional Data"; I am interested in understanding batch K-means and ...
2
votes
1answer
127 views

PAM with Gower distance matrix

My data is is mostly continuous but has one binary variable. I tried the pam algorithm in R with the Gower index, but the number of clusters that give the best ...
5
votes
0answers
54 views

Territories from observations

I have a number of animal observations, and want to deduce the number of territories (i.e. the number of individual animals) from this. More formally, the problem can be stated as follows: Each ...
2
votes
1answer
76 views

Kmeans algorithm cyclical solution

I am currently implementing a Kmeans clustering algorithm in R. I am not using any packages and I wrote it from scratch. I am using only one set of initial guesses, and my action upon finding an empty ...
7
votes
2answers
157 views

Adverse results of clustering criteria

I have carried out a clustering of coordinate points (longitude, latitude) and found surprising, adverse results from clustering criteria for the optimal number of clusters. The criteria are taken ...
2
votes
1answer
111 views

How to estimate the centroid of clustered sequences?

I have run a sequence analaysis using the Optimal Matching algorithm. Afterwards, I have clustered the resulting distance matrice using the Ward algorithm and calculated silhouettes as measures of ...
4
votes
1answer
370 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 ...
3
votes
1answer
389 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 ...
0
votes
1answer
74 views

Enrichment of variables within clusters

I'm trying to find if some clusters in an arbitrary annotated dendrogram are enriched for a particular level of a categorical variable . At the moment, as you can see in the picture, I'm simply ...
3
votes
2answers
196 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. ...
4
votes
0answers
65 views

Which variables are driving correlations within groups

I'm running an analysis on a few data sets that each typically have 100-200 cases measured across 120-160 variables - something similar to looking at gene expressions. Each variable is a non-centered ...
4
votes
1answer
319 views

Select best set of binary variables for clustering known sample labels

I have a set of samples, for which I know the "true groups". For this samples I have about 200 binary variables, I would like to know a method to select the subset of variables, that gives me a ...
0
votes
1answer
203 views

Fit of a normal distribution to a one-dimensional dataset in R

I've got a set of (continuous) values from a measurement, where each object should be either positive or negative, and I know that the values of the "negative" objects should be approximately normally ...