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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16 views

Best clustering algorithm for real state data

I want to cluster real state data to determine average price patterns in city and rural regions. My data set contains size, number of dorms, bathrooms and coordinates of the properties. Which would ...
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13 views

comebine F1-measure and Hubert's Γ clustatistics [on hold]

I'm confused about two cluster validation formula in this paper it's in CO-Smart-CAST algorithm line 10-12 why do we need to calculate $Γ_{clu}$ and $Γ_{obj}$ and then combine them with F-score? ...
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22 views

Which kind of analysis could be made to associate a set of genes to clinical values?

I have a set of 5 genes that can be mutated or not, so therefore are intended as dichotomous yes/no vars. I want to identify the effect of the mutation of this genes on a continuous response var. The ...
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0answers
10 views

Profiling high-scoring clusters in a multi-dimensional feature space

I have a large amount of samples, which have a multi-imensional feature vector associated with them. Each sample has a "score", and the length of the feature vector is substantial (n>100, and in ...
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26 views

Investigate correlation between one variable and combinations of others

We're conducting a study which correlate the incidence of various conditions during pregnancy and in newborns and the use of artificial reproduction technique (ART). This way we saw that some ...
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1answer
103 views

Clustering data into bins of variable sizes

I'd like to build a model (in R or excel) that takes in large amount of linear data and segments it into "bins". The linear data is an attribute that reflects what condition that section/record is at. ...
3
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0answers
34 views

Use of Hidden Markov Models for Clustering

I would like to ask whether Hidden Markov Models can be used for clustering and if so, in what cases. I have found somewhere, references like this but practically I haven't found a way to do this. Is ...
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13 views

Automatically classifying user activity/sessions on a website?

X-posted from Stack Overflow: I have a large body of records pertaining to user activity on a website. What I want to do is some sort of classification on each user as they navigate my website. Every ...
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7 views

Good mean shift clustering datasets

I am working on a modification of the Mean Shift algorithm and would like to validate my clustering results. I am struggling to find suitable clustering datasets to compare performance with. I'm ...
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18 views

Observation/case weighting in cluster analysis

Sampling weights, the inverse probability of a unit's selection into the sample, and other more complex and adjusted weights are very often used in the social sciences. There is statistical software ...
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1answer
24 views

What Grouping Method To Determine Average Over Lifetime?

I have the following data: When individual 'x' joined a company. As the data is limited to 2 years I do not know the start date of every individual. When individual 'x' left the same company. If this ...
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1answer
23 views

Cluster Sequences of data with different length

I need to cluster sequences of data that have different length. I am using Matlab and my first question is related to the method. Is KMeans sufficient to achieve this? IN KMeans I have to use the ...
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15 views

Logistic regression and cluster ID

The dataset consists of all prescriptions classified as on or off-label (0 or 1), meaning possible more than one prescription per child (pnr-number) I want to know the off-label rates per year for ...
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1answer
31 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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19 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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16 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
58 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
29 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
58 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
41 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
39 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
29 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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18 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
22 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
32 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
50 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
72 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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25 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
37 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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21 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
11 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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20 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 ...
1
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1answer
27 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
60 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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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
11 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
12 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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21 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
28 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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23 views

Can we use kNN and k-mean at a same time?

I Get dataset of neighbours using kNN and then I want to apply k-mean on that dataset. By using this, is it possible that I get more accurate result? Is it logically correct that use kNN and then ...
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0answers
13 views

post-hoc testing in clustering analysis 1 feature [closed]

I've read that a post-hoc testing of the results of a clustering analysis is incorrect to perform. If I understood well this is true if the data are multidimensional and the purpose is to find out ...
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0answers
27 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 ...
5
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1answer
99 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
13 views

Ward's clustering with Gower's metric [duplicate]

I calculated Gower distance matrix with daisy(cluster) function in R and than applied K-medoids with pam(cluster) function. I tried for cluster number k=3,4,5,6...20. But average silhouettes were ...
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0answers
37 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
25 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
27 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
26 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 ...