1
vote
1answer
91 views

Pull out most important variables from PCA

I would like to get the most important variables from a PCA result. I see two clusters in the plot. I now that is possible that there is no only one variable causing this, so maybe I would have to get ...
0
votes
1answer
33 views

using cluster information in multiple imputation

i need to impute a dataset all categorical variables before doing analysis. I can just simply impute with mode of all data or a variable. I belief that better option will be to classify the subjects ...
0
votes
0answers
44 views

(Spatial) distance between cluster means

I'd like to cluster points based on a distance criteria. As I want to cluster spatial points I am using euclidean distance and a hierachical cluster approach. In a final step I'd like to cut the ...
0
votes
0answers
21 views

Spectral clustering using RBF Kernel function in R

I have extracted user-features and item features in my recommender system using a modified SVD approach built on ALSE (loosely based on Yehuda Koren's paper). I now want to cluster items not directly ...
0
votes
0answers
21 views

Performance of hierarchical clustering for binary data in R

I am trying to use Hierarchical clustering to see how well it performs in classifying a dataset which I previously know its true classification. I am new to clustering in general. I was able to draw ...
1
vote
0answers
27 views

Cophenetic distance matrix to a dendrogram

In hierarchical clustering procedure, a distance matrix is used to construct a dendrogram with an appropriate method of clustering. In the process of constructing a dendrogram, a cophenetic matrix is ...
1
vote
1answer
26 views

Extract (ultrametric) distances from hclust or dendrogram

How can the matrix of (ultrametric) distances be extracted from the result of hclust (or a dendrogram in general) in R? The ...
0
votes
0answers
25 views

Implementation for Co-Clustering

I am looking for existing implementations for co-clustering (aka biclustering). I came up with biclust function available in MATLAB, but still I am wondering if ...
1
vote
1answer
41 views

Different hierarchical clustering results

I'm running a hierarchical clustering on a sample of data using the steps below: ...
0
votes
0answers
41 views

Computing a distance matrix between multiple multivariate time series

This question has also been asked on stackoverflow.com. Yet my aim is to ask for efficiency gains on the aforementioned platform. My aim here is the correctness of my approach. I am trying to cluster ...
2
votes
1answer
57 views

Is there a decision-tree-like algorithm for unsupervised clustering?

I have a dataset consists of 5 features : A, B, C, D, E. They are all numeric values. Instead of doing a density-based clustering, what I want to do is to cluster the data in a decision-tree-like ...
0
votes
1answer
76 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 ...
3
votes
1answer
158 views

Using PC scores or cluster analsis in predictions

I have very big data and low number of observations. So I decided to use PCA to reduce dimension of the data. The following is R example (just an dummy example - for workout): ...
1
vote
1answer
28 views

Similarity between different length vectors containing related items

I have a vector (V1) with which I need to calculate the similarity of other vectors (ex V2,V3 ... ) which may be of different lengths. The different angle here is that the elements inside the vectors ...
1
vote
1answer
58 views

Approximating a Complementary Cumulative Distribution Function via a piece-wise function

I hope this is not too much to read, but I tried to give you a specific overview over my problem. I am currently trying to model the German electricity market, with a special focus on balancing ...
2
votes
2answers
93 views

How can I cluster data in a grid-like fashion and heat map the averages in R?

I have a data frame of 3 columns. The first one is the response variable the second and the third ones are some criteria. You can create your own example similar to mine, using this piece of code with ...
2
votes
1answer
69 views

Is there a package that I can use in order to get rules for a target outcome in R

For example In this given data set I would like to get the best values of each variable that will yield a pre-set value of "percentage" : for example I need that the value of "percentage" will be ...
4
votes
1answer
75 views

Clustering data that has mixture of continuous and categorical variabes

I have data that represent some aspect of human behavior. I want to cluster it (unsupervised) into behavioral profiles of some sort. now, some of my variables are categorical (with 2 or more ...
0
votes
1answer
50 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
1answer
35 views

Cluster analysis

I am trying to cluster cells (1×1km) over a specific area. Each cell is composed of various habitats defined by a code. (Each habitat consists of 3 parameters, so a habitat code looks like e.g. ...
2
votes
1answer
87 views
4
votes
2answers
113 views

Is there an advantage to squaring dissimilarities when using Ward clustering?

Is there a reason to prefer squaring or not squaring the dissimilarities when clustering with Ward's method? The question is motivated by the following statement in the documentation for R's ...
0
votes
1answer
24 views

Algorithms that use multiscaler properties of data to cluster

I was thinking of devising a clustering algorithm (for fun and kicks) that would cluster data by looking at the distribution of the data at multiple scales. For example say my data was distributed on ...
0
votes
3answers
73 views

Problem with PCA

I am trying to do some PC analysis on my data coming from lipids measurements in different samples. I only have one factor: if samples are diabetic or non-dibetic. Here is the PCA graph I get: As ...
-1
votes
1answer
52 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
81 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
56 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
45 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
25 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
160 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
44 views

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

consider the following example data: ...
2
votes
0answers
38 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
44 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
63 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
111 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
46 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
42 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
238 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
674 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
55 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
77 views

Clustering 2d data using kernel density methods

Assume I have data looking like this ...
2
votes
1answer
484 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
138 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
149 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
150 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
206 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
105 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
200 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 ...
4
votes
1answer
280 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
151 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 ...