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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288 views

An incremental Gaussian mixture model

Question 1: Suppose that data is modelled by a mixture of K probability distributions which are actually Gaussians. $P(x_i|\theta_j)$ is the probability density of the j'th cluster, for which the ...
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31 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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21 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
123 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 ...
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20 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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1answer
72 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 ...
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15 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
10 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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15 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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2answers
71 views

k-means cluster, How to re-calculate centroid when using cosine similarity?

I have a requirement using k-means cluster method with cosine similarity instead of Euclidean distance. for example: ...
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2answers
41 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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1answer
20 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
553 views

Image Clustering with K-means - Postprocessing

I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. ...
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4 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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3answers
853 views

Clustering with K-Means and EM: how are they related?

I have studied algorithms for clustering data (unsupervised learning): EM, and k-means. I keep reading the following : k-means is a variant of EM, with the assumptions that clusters are ...
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1answer
330 views

F-measure for document clustering evaluation - NaN

I'm developing the Java application for text document clustering, and I'm researching some evaluation methods. I implemented F-measure (http://en.wikipedia.org/wiki/F1_score), but I have a problem - ...
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2answers
1k views

PyMC for nonparametric clustering: Dirichlet process to estimate Gaussian mixture's parameters fails to cluster

Problem setup One of the first toy problems I wanted to apply PyMC to is nonparametric clustering: given some data, model it as a Gaussian mixture, and learn the number of clusters and each cluster's ...
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1answer
199 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 ...
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2answers
338 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 ...
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70 views

Combine Clustering and classification

I have a receipt database of a grocery store. I would like to find classes of similar customers based on their receipts and classify people after their shopping to one of these classes. Let us assume ...
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10 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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8 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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3answers
733 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 ...
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13 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
16 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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18 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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6 views

post-hoc testing in clustering analysis 1 feature

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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3answers
106 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 ...
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32 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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1answer
82 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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1answer
625 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 ...
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1answer
82 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. ...
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25 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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21 views

Dealing with outliers: Clustering

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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3answers
301 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 ...
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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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22 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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26 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
92 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., ...
4
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1answer
738 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 ...
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1answer
199 views

Understanding the construction of Dirichlet process

I'm trying to understand the construction process of DP, however, with little background in measure theory, the original papers are hard to read, but I believe the ideas behind these papers can be ...
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1answer
24 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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1answer
153 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 ...
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1answer
34 views

How to cluster data with repeated measurements?

Most clustering algorithms assume that data points in each row are independent. I have some data with repeated measurements from individuals. I can use a standard algorithm, and then check to see if ...
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17 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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1answer
47 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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12 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: ...
2
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1answer
24 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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11 views

Advantages of using spectral clustering

One use of spectral clustering is that it is applicable to situations where distance is non metric. What are the other advantages of using spectral clustering?
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10 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: ...