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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Analysing ranked data

I had following question in my questionnaire: Rank following factors: price, quality, advertisement, brand, reference from 1 (very important) to 5 (least important) that influenced on your buying ...
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which distance should be used with UPGMA clustering

I am trying to cluster a biological population on the basis of morphological characters using UPGMA clustering method, but I am not sure which distance should I use- Mahalanobis or Euclidean. What are ...
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How can you cluster a set of functions with unknown functional forms?

Say you've $N$ functions $f_N(x)$ defined on a regular grid $x$. You don't know the form of $f(x)$, you've only got several realizations of it. The different functions are related to each other ...
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Clustering with Restricted Boltzmann Machine

I am working with the basic RBM that can be found on Geoffrey Hinton's webseite and the MNIST dataset. What I want to do is graphically cluster the input data. I am working with a three layer network ...
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14 views

Cluster analysis for multi-response question

Let's say I have check-all-that-apply survey question. What kind of analysis can I run to understand if there are meaningful clusters (i.e. there's a cluster of people who choose A, B, C, and another ...
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26 views

Feature selection for transductive learning

I have a large data set with 30% labelled samples and 70% unlabelled samples. Each sample is a 60-dim vector. My idea is to apply transductive learning to try to label as much as samples as possible, ...
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11 views

Grouping search queries by similarity of search results

we run some studies using google search queries. We need to cluster these queries in topics and we would like to find some unsupervised approach. for each query we have the search results. I ...
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26 views

find group of rows in a matrix with pattern similar to another matrix

I've been scratching my head about this problem for some time: I have a big gene expression dataset (20k genes x 200 samples) in matrix A and i have a subset of this dataset (i.e. 40 genes x 200 ...
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23 views

How can I calculate cosine distance with multiple feature vectors and weigh them?

I have a dataset of text documents and I'm calculating pairwise cosine distances among them. For each document I have a bag of words vector, a vector built from entities extracted from the document, ...
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What are Clustering techniques for this case? [duplicate]

What type of clustering methods are available for ordinal, nominal and ratio variables? Suppose I have one ordinal, one nominal and one ratio variable; is there a common clustering technique that can ...
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Dirichlet process mixture model in Python

My question is concerned with the practical issues of using this model. I've tried to use Dirichlet process mixture model from Scikit learn python package to find a number of clusters in my data (1D ...
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35 views

How to represent outliers for multi dimensional data (local outlier factor)

Below graph taken from http://en.wikipedia.org/wiki/Local_outlier_factor displays "LOF scores : LOF image : This is great for two dimensional data but what about data > than two dimensions. How ...
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47 views

How to evaluate a clustering/unsupervised learning problem with massive amounts of data, with labels only for a small fraction of points

I'm wondering if anybody can point me to work on the evaluation of unsupervised learning where there are a very large (say hundreds of millions) number of points and manual labelling can only ever be ...
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How can one evaluate Incremental Clustering Algorithms, in particular the goodness of the clusters formed?

I have been studying an incremental clustering algorithm for a large set of data that exhibit an inherent dynamic behavior (that is new data can get added over time and some older data may get deleted ...
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1answer
17 views

Generating even-sized clusters in scikit-learn [duplicate]

I'm attempting to generate approximately even-sized clusters of a PCA'd feature set in Scikit-learn, but I'm not having any luck. I'm only familiar with KMeans clustering, and with that algorithm the ...
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23 views

Statistical significance of cluster validity

Hi I m working on a unsupervised problem to partition my dataset. I have access to the class labels for this dataset. Now I am trying to use Jaccard coefficient to compute correlation between cluster ...
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23 views

Spatial cluster analysis

Let's say I have a structure like this : This is a spatial region with measurement of plant population in each site. Black and red represent two regions with different intensities.The question is ...
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25 views

Cluster migration visualization

I have asked a very similar question at the Latex forum here, but in order to address the part of my question where I ask if there is a better way of visualizing the data I have, I wanted to cross ...
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27 views

Kmeans cluster size change quite a bit on each run

I am running a kmeans on a sample size of 1000 data. The data is scaled (z). When I run kmeans(df, nstart=25, centers=5)- it runs and I can get the size of each cluster. The largest group has 620 in ...
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Heteroskedasticity in a Linear Mixed Model SAS PROC MIXED

Asked a version of this question before but realized it needed some clarification. I have a dataset with identical twin pairs and fraternal twin pairs. I want to examine the relationship between an ...
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Cluster analysis (proximities)

I have a question regarding clustering. I have a symmetric matrix of 50 specialties (50 X 50) where each cell represents the number of observations related to each combination of specialties. Some ...
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17 views

Kmeans plotting on discriminant components

When you plot a kmeans model (in R) with the plotcluster() function, it plots the clusters against the axis of the 1st and 2nd discriminant components (dc). In reading about these axis- some state ...
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24 views

How to do clustering using genetic algorithm?

I am studying how to use genetic algorithm (GA) in clustering analysis on R programming. What I understand now is that we have to determine fitness function in GA. That is, we have to minimize within ...
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33 views

Variable Clustering Analysis

I have a data set that consists of 143 variables (~11000 observations), and I wish to do variable clustering to reduce the dimension. I am using hclustvar function ...
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1answer
28 views

Autoclass in R/Python? [closed]

Are there any packages that implement the Autoclass/ Naive Bayes Clustering algorithm in R or Python? Alternatively, what are some other clustering algorithms that can handle both categorical and ...
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How to propositionalize a relational data set for clustering analysis?

I am working with a data set of students and their courses for a single semester, attempting to cluster based on the courses & various other attributes where "courses" are the "many" side of a ...
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Clustering using different distance measures [duplicate]

I do unsupervised clustering for a dataset using k-means algorithm. I want to know what is the difference between different distance measures (Euclidean, cityblock, cosine and correlation,...etc). I ...
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52 views

PCA scores and cluster analysis

I have obtained PCA scores (Anderson-Rubin) and thought to use them in cluster analysis, but have got confused how to make interpretations based on them or as what type of variable they should be ...
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How to include noise in clustering evaluation?

When evaluating clustering methods which do have a definition for noise points (like dbscan), how noise will affect evaluation? Consider a clean dataset like well known Iris dataset. There is no ...
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28 views

How to compare two clusterings generated by two clustering approaches

I am currently working on a modification of a clustering algorithm to suit my problem domain. I want to know which methods are available for me to compare the centroids generated from the two ...
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20 views

Interpreting R results, are the data multivariate normal?

I ran "mvn" using the "mclust" package in R using the following codes: mvn("EEE", data[,18:22], prior = NULL, warn= NULL) I am having trouble figuring out how to ...
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1answer
29 views

Testing the hypothesis on clustering

I have a number of samples. For each, there is a time course of multivariate data defined, with $t$ timepoints ($t < 50$) and $n$ variables ($n > 100$). We have noted that the time courses of a ...
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1answer
37 views

Comparing mclust() and k-means centroids

I have some code that looks for clusters in x,y data. To check the number of clusters I use, I want to get the BIC. This is not possible (easily) using kmeans(), ...
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45 views

why Hierarchical Clustering pvclust vs. hclust got different result?

I am performing the hierarchical clustering analysis on a dataset of 25 viral populations using 3 viral components (variables) to construct a dendrogram with average method and correlation distance ...
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38 views

R codes for variation of information criterion using “mclust”

I am developing model-based clustering. First, I developed model-based clustering in R using "mclust." Next, I wanted to take 75% of the sample, re-run model-based clustering and compare the ...
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Appropriate PCA/ EFA rotation method

Question about appropriate PCA/ EFA rotation method. While I see that oblique rotation methods (e.g. promax) are suggested for correlated data (and I have correlated data) I see that the majority of ...
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Cluster Matching/comparison

Description I have several datasets(from different subjects) with the same type of data. For each dataset I cluster the data using affinity propagation. Clustering is based on similarity distance ...
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1answer
28 views

Binomial data and PCA and cluster analysis

I have obtained responses on around 48 items measuring employer attractiveness. The goal of my analysis is to cluster respondents according to these employer attractiveness dimensions. I plan to ...
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Streaming k means clustering Mahout

First of all, excuse me if this is not a good place to ask this question. Can anyone explain to me how streaming kmeans algorithm implemented in Mahout works? And can it be used for anomaly ...
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Term to Describe Strongly Clustered Data

I have some data which are strongly gathered into more than one cluster. I am looking for a term to effectively describe this phenomenon: e.g., multi-clustered data, which however seems to me that we ...
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1answer
19 views

Correlation or clustering of continuous score and discrete variable states

I have an experiment that produces a decimal score representing quality, and a bunch (5-30) of variables that each take on one of a set of discrete states. - The states are not meaningfully ...
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1answer
31 views

How can I determine local minima from a Kernel Density Estimation? [duplicate]

I have a fairly large 1-dimensional array that I am trying to cluster. I came across several other questions on this site where the top answer is to use a Kernel Density Estimation and then locate ...
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1answer
27 views

Mixing probabilities in mixture models using EM

Assume we want to estimate the mixing probabilities ($\pi_{k}$) for each member distribution in the mixture model. We know that $\sum_{m}^{K}\pi_{m}=1$, so we can formulate the optimization problem ...
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Using k-means with other metrics

So I realize this has been asked before: e.g. What are the use cases related to cluster analysis of different distance metrics? but I've found the answers somewhat contradictory to what is suggested ...
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23 views

breakpoint analyses on multiple series: how to detect common points

I have 20 time series that span the same period (100 days each), from 4 species sampled at 5 different location. I made a loop to perform a breakpoint analysis on all of them, resulting in 0 to 3 ...
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48 views

How to Calculate silhouette coefficient in SPSS for clustered data set?

I am having a pre clustered dataset with data and the action cluster identified for it using a custom clustering method. I am looking to calculate silhouette coefficient on this clustered dataset ...
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46 views

What's a good way to mentally visualize n dimensions in a k means

I've been using k-means to do some clustering and one of the ideas I'm struggling with is the n dimensions aspect. If I were clustering housing prices vs sq. feet its just a simple 2d graph. That I ...
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1answer
25 views

Is my understanding of how to calculate the reachability distance in local outlier factor correct?

Reading lof implementation at : http://www.cse.ust.hk/~leichen/courses/msc-it5210/lectures/LOF_Example.pdf the local reachability distance is given as : I don't fully understand this equation as ...
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Clustering techniques when there are two distinct but numerically similar clusters among others

I'm trying to cluster some spatial statistics for some behavioral estimation. However, parts of the trajectory report exactly 0 speed, which implies some kind of resting state which is different from ...
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Why is it called Epsilon for DBSCAN?

The two parameters of DBSCAN are epsilon and minimum samples. Shouldn't epsilon be called like "Circle radius"? Why is it called epsilon?