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.

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38 views

Hierarchical clustering: different result when I change labels

I am running hierarchical clustering with a distance matrix M_norm: hc <- hclust(M_norm^2, method = "ward.D") plot(hc, cex = 1, hang = -1) When I use ...
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27 views

Descriptive clustering of papers

Given a set of PubMed abstracts or keywords derived from MeSH terms, I would like to know how many and what topics are among them in order to write a paper review. Other information such as the number ...
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0answers
22 views

The best algorithm for short documents clustering

I have a corpus of short text documents. Each document is an automatic recognized phone conversation (a dialog) from a large call center. The texts are not clean and have lots of grammar and other ...
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1answer
74 views

A valid distance metric for high dimensional data

I asked a question about forming a valid distance metric yesterday (Link1) and got some very good answer; however, I have got some more questions about forming a proper distance metric for high ...
3
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1answer
37 views

Can I use k-means with a distance matrix composed of percentages? [duplicate]

I have objects o1, o2,...,on and for each pair I calculate a value that measures the pair's difference. This is a percentage, so for example o1o2 differ by 56%. Now I want to cluster this data. I can ...
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3answers
75 views

How to get a valid distance metric?

I have got a problem to devise a distance metric to get the similarity measurement of vectors. Someone suggested me to use dot product, which seems to me the same as the Cosine similarity metric; ...
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0answers
64 views

INTRA-cluster and inter-cluster distance

Thank you for your reply I have generated a valid partition using the following code IDX = kmeans(data(:, 1:end-1),k,'replicates',10,'EmptyAction','drop'); and I am comparing the intra and ...
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6 views

The authenticity of the N-cut measure when the number of components in the data is high

I'm running a clustering task on unlabeled data, and assume we're validating our results by applying the Min-Cut measure as an internal validity index. Let's refer the normalized version of the ...
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0answers
51 views

clustering vs fitting with a distribution

I have a question about using a clustering method vs fitting the same data with a distribution. Assuming that I have a dataset with 2 features (feat_A and feat_B) and let's assume that I use a ...
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0answers
55 views

Middle point between k-means and DBSCAN in R

I have a big data sample of unrelated events in lon,lat,date format (booking locations to dispatch). I am trying to divide these events into clusters (k=50) where I ...
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0answers
19 views

How to measure whether the number of points belonging to a cluster is statistically significant

I have a set of data in five clusters (say C1 through C5). From this, I also have the probability of a random point belonging to each of these clusters (p1% through p5%). I select a subset of my ...
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1answer
30 views

Unnatural clustering with known clusters shapes and optimization criteria

My question is similar to this question Clustering with shape prior, but with additional information. The second answer suggests a mixture model approach to this problem, which is something like ...
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2answers
48 views

Generate a random chain with cauchy distribution using C language

Here is my question: I want to simulate a random variable using cauchy distribution with C language. Scale and position must be setted manually. I fuond the GSL library wich contain the function: ...
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1answer
62 views

What kernel function can be used to project data into a feature space that is a “circle”?

I am working with cyclical data (Days 1-7, hours 1-24). I want to project it into a feature space that can understand that 1 and 7 are close days and 1 and 24 are closer than 22 and 24, etc, and then ...
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1answer
75 views

Distribution of p-values in this thought experiment?

I'm trying to check whether my clustering was informative above and beyond random clustering. This is my thought experiment to do it, can someone help? Suppose I have a large number, $N$, groups. ...
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0answers
34 views

How to analyse a factor experiment with feature extraction, clustering and classification algorithms as factors?

Currently I am doing my final project, which consists of designing an experiment to test several combinations of algorithms on a dataset, such as feature extraction, clustering, classifiers and ...
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1answer
42 views

After Clustering, how can I evaluate which features had the biggest impact?

I've just performed unsupervised clustering (using DBSCAN) on a dataset for which I have no expert knowledge on. I'm interested in working out which features had the greatest impact on my clustering. ...
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0answers
22 views

How do I find corresponding clusters in independent samples?

Lets suppose you believe that observations in your data come from K natural but not directly observable categories and you wish to identify these categories with minimal prior assumptions, so you find ...
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1answer
18 views

Why do two identical feature vectors (distance score 0) get different labels in DBSCAN?

I have two identical feature vectors. They have a distance score of 0. I perform DBSCAN Clustering (using sci-kit) and they get different labels. Is this expected behaviour?
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42 views

The best way for clustering an adjacency matrix

I've had a hard time interpreting resulting clusters of an adjacency matrix. I have 200 relatively big matrices representing subjects that contains partial correlations (z scores) of time series ...
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1answer
34 views

Efficient weighted 1D Clustering (Grouping)

I'm dealing with the simple problem of grouping a set of 1-dimensional data (1 feature) according to its distribution in the 1-D space. I know exactly the number of groups I will like to get. So for ...
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2answers
79 views

k-mean clustering of week-times

I have data of meeting times. The data has weekday and hour of the day. I want to cluster the meeting times (I have reason to believe there are two different kinds of meetings that tend to occur at ...
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0answers
5 views

finding critical categories within index

There exists network readiness index (NRI). 10 categories forms this index. Each category has subindicators. NRI index is measure four more than 100 countries. My goal is to identify critical ...
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0answers
13 views

What is known about the efficiency of spectral clustering in case the data is not fully connected?

My question stems from the fact that the normalized cut can be very low in case we have a lot of connected components, because the cost of "cuts" is zero which eventually will lead to a a low N-cut ...
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1answer
15 views

Validity index for non convex clusters

I'm trying to measure clustering methods on unsupervised data. Among others, I'm using DBSCAN which can find non centered clusters. Is there any internal cluster validity index fitted for non-convex ...
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0answers
49 views

Using Canonical Correlation Analysis (instead of EFA/PCA) to reduce the dimensionality of two sets of variables prior to clustering/classification

I have two sets of paired continuous data obtained from two tests. My goal is to answer the following research questions: Q1. To what extent can results on one test be used to predict the results on ...
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1answer
43 views

R - how to transform the similarity matrix to distance matrix for performing hierarchical clustering?

I am trying to cluster nodes (C1, C2, C3...) of a graph using hclust and my similarity metric is number of links between nodes. I have data like ...
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33 views

Deciding overall similarity measure at each step of hierarchical clustering in r

I want to decide the appropriate number of clusters after using hclust() and drawing a dendogram. I don't have any idea about how many clusters should be there ...
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1answer
114 views

Meaningful inference about data structure based on components with low variance in PCA

A lot of microbiome (microbial ecology) papers that I have come across use either principal component analysis (PCA) or principal coordinate analysis (PCoA) to make conclusions about the data. A lot ...
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0answers
24 views

Dealing with outliers: Clustering [duplicate]

I am working with a dataset in R that I will be doing cluster analysis on and I am trying to determine the best way to deal with the outliers.I have twelve variables and most variables have between ...
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0answers
38 views

Non-decaying eigenvalues in Kernel PCA with small kernel width

I noticed that when I use a small width kernel (RBF) with PCA, I get my desired result (clustering in this case), but I do not get a decay in the eigenvalues (they stay about the same value). Is that ...
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0answers
30 views

Normalization in case of clustering

Can normalization in this form be used (x−μ)/σ and should it be used in case of clustering? I have parameters on different scales and since I'm calculating the distances I need to perform feature ...
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0answers
29 views

Clustering based on correlations between survey questions

I'm trying to analyze a survey and find the questions that are most often answered in the same way. There are 29 questions, and I have a matrix with the correlations between each pair of variables. I ...
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1answer
28 views

Normalization problems - how to normalize in case of set of points while new points arriving

I'm having a procedure in which I perform clustering, and later, for each new example I test if that example belongs to some of existing clusters, by calculating distance to existing centroids. To ...
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19 views

Gaussian based clustering/partitioning, does it make sense with not much data?

I have a dataset about hourly aggregated mobile phone usage (#calls, #sms, #internetConnections) in one mobile cell. For example I have this data about activity at 8:00am: ...
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15 views

which distance for clustering

I've got 6000 reports that I've cleaned up. I've used 8 different steps to remove words of the reports. For each report, I've got a table of the following form: ...
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1answer
27 views

Can nonlinear clustering produce 'fake' results?

I know that overfitting in classification is possible when using, for instance, an RBF kernel, due to its infinite dimension. But, is it possible to get (in a similar manner) fake clustering results ...
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9 views

Using Quality metrics of BIRCH Clusters

What is significance of quality metrics of BIRCH Clusters Distance3 and Distance4. Appreciate if there are pointers are how to use Average Intra Cluster Distance (D3) and Average Inter Cluster ...
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0answers
18 views

Testing Cluster Assignment/Pattern Matching against BIRCH Clusters

I have a dataset of size >35K in size / >50 dimensions. Used BIRCH algorithm for clustering. While testing, the data points with which cluster formed is not matching i.e., The data point shows closer ...
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1answer
65 views

Why is k-medians typically used with Manhattan rather than Euclidean distance?

K-medians is typically used with Manhattan distance rather than Euclidean distance. Why is this?
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1answer
53 views

K means clustering of variable with multiple values

I have a sample data below that is from a large data set, where each participant is given multiple condition for scoring. ...
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1answer
11 views

Clustering Data majority is 0

I am performing a cluster analysis with a 4K by 200+ table and my data mostly looks like this: ...
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1answer
318 views

Gower distance with R functions; “gower.dist” and “daisy”

I have 9 numeric and 5 binary (0-1) variables, with 73 samples in my dataset. I know that the Gower distance is a good metric for datasets with mixed variables. I tried both daisy(cluster) and ...
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0answers
24 views

Normalize data for clustering

I am trying to perform clustering (planned to use K-means in R) on the data that contain both categorical and continuous variables. For example, my data contains 4 variables: gender (M and F), income ...
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1answer
67 views

PCA clustering results 'ruined' by standartization

I have some data that I want to classify. As an initial measure, I did PCA for the data and I saw two distinct clusters of my data. However, when standardizing the data, the two clusters disappear. ...
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45 views

K-means and maximum likelihood!

Is there any relation between k-means and the maximum-likelihood estimate in unsupervised learning? Any references would be appreciates! Thank you!
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13 views

Clustering cases on variables discovered in-sample via factor analysis?

My Data I have 2-hourly readings on approximately 10K sensors taken over the course of a year. The resulting time series look pretty similar day to day (though there are some longer term trends), and ...
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0answers
30 views

How the quality of clusters made in SPSS can be evaluated?

How the quality of clusters made in SPSS with the method "Two-step clustering" can be evaluated? Which test should be applied to be sure that the quality is good.
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0answers
39 views

Determining number of clusters using Hadoop and Mahout

I would like to use Mahouts clustering algorithm, such as Streaming K Means. But the thing is, this algorithm (and others such as Lloyd KMeans) require to specify the number of clusters. I have read ...
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2answers
203 views

Clustering a long list of strings (words) into similarity groups

I have the following problem at hand: I have a very long list of words, possibly names, surnames, etc. I need to cluster this word list, such that similar words, for example words with similar edit ...