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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Text clustering papers with Precision and Recall

I have created a text clustering algorithm and calculated the Precision and Recall measures for the evaluation. I am looking for papers that contain other text clustering algorithms evaluations with ...
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46 views

Finding the best dataset for classification

I have 100 datasets. All of them have varying number of features. There are around 20,000 samples in each of them. Every $i$-th sample in the 100 datasets has the same label ($0/1$). The data is ...
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541 views

Why does k -means clustering algorithm use only euclidean distance metric?

Is there a specific purpose in terms of efficiency, functionality why k-means algorithm do not use cosine similarity as a distance metric and use the euclidean norm
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169 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. ...
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27 views

Face Recognition using NMF

I am trying to implement a simple face recognition software using nonnegative matrix factorization. I have tried this on the ORL dataset which has 400 face images (40 people * 10 photographs). The ...
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81 views

Detecting latent relational clusters (a.k.a blockmodeling) (PyMC)

By looking at the set of relationships within a community, we might discover that we can divide them in groups where people in the same group (a.k.a block or role) tend to relate to the same other ...
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89 views

Cluster analysis, what to do with different scales?

I want to identify different groups of respondents out of up to five variables of the European Values Study 2008. At first I took 4 questions for cluster analysis all on a scale from 1 to 10. ...
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86 views

Weighting variables in two-step cluster analysis

I'm using SPSS to perform two-step cluster analyses. SPSS shows predictor importance of each variable used in an analysis. Oftentimes, a binary variable like gender (sorry, I'm just keeping it ...
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398 views

Clustering with Weka

Hi everyone and happy new year! I have to analyse a data set with weka clustering, using 3 clustering algorithms and I need to provide a comparison between them about their performance and ...
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67 views

Choosing the right clustering method with the number of clusters specified

I see a couple of questions on stats.SE regrading finding the optimal number of clusters for a clustering problem. However I do not want the clustering algorithm to do that for me. Which algorithms ...
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133 views

Why only the mean value is used in (K-means) clustering method?

In clustering methods such as K-means, the euclidean distance is the metric to use. As a result, we only calculate the mean values within each cluster. And then adjustments are made on the elements ...
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68 views

Pattern Clustering

I have data of daily time spent in studying for around 2000 students. I need to make clusters based on the pattern of hour spent in a week, not by average hour spent.For example some students are ...
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82 views

Relationship between dimentionality reduction and clustering algorithms

I've got bit confused about dimensionality reduction and clustering . whether all clustering algorithms (k-means, affinity propagation, spectral clustering,...) do kind of dimensionality reduction ?
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58 views

Graph Theory - Creating an Index of Familiarity, Given Trade Frequency Counts

Set Up I'm hoping to create an "index of familiarity" between traders on a barter market. I have data from a peer-to-peer barter market (i.e. people come in with their wares, and can trade with ...
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Are there any good papers comparing different philosophical views of cluster analysis?

Lots of people use cluster analysis. I've heard very few explicitly say why. I imagine this is because within a given field, most researchers seem to understand why clustering is used for the problems ...
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21 views

Scaled graph laplacian in presence of loops

I am interested in spectral clustering so I was looking the scikit-learn code for computing the Lapacian of a graph given its weighted adjacency matrix. ...
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109 views

Strategy for finding cluster solution with positive values for Cubic Clustering Criterion

I am trying to cluster a data set with approximately 3500 entries, consisting primarily of likert-10 scale survey answers. Most of the survey answers are skewed - a typical example is below: I've ...
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80 views

The right distance for the clustering. Maybe Mahalanobis?

I have to do a cluster analysis and I'm asking which distance should I used. I know that 99% of the clustering are made using a euclidean distance, but I heard about the Mahalanobis distance and it ...
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85 views

How to interpret these indices/metrics for comparing partitions intuitively out of these images?

Two sets of comparisons were performed between original clustering and the new clustering using several indices and metrics of performance. Below are the two initial clusterings or partitions (these ...
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133 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 ...
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84 views

How to implement hierarchical clustering in $O(N^2)$ instead on $O(N^3)$

First a theoretical question. I know that natively, an hierarchical clustering algorithm is of complexity on the cube of number of samples N. This is due to the fact that in each iteration, one has to ...
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83 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 ...
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58 views

Cluster analysis for few data points

I have a question regarding cluster analysis. I was wondering if I have only 9 data points, is it valid to use k-means methods in cluster analysis? I have done a special molecular evolution ...
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242 views

How to compare dbscan clusters / choose epsilon parameter

I am currently trying to make a DBSCAN clustering using scikit learn in python. I would like to compare the different outputs when varying the epsilon parameter in order to choose the right epsilon ...
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139 views

How to find the number of clusters in 1d data and the mean of each

We have a list of prices and need to find both the number of clusters (or intervals) and the mean price of each cluster (or interval). The only constraint is that we want cluster means to be at least ...
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33 views

How many different random initializations should I perform with Lloyd's algorithm to obtain the optimal clustering with X% of confidence?

I use Lloyd's algorithm for clustering. Since it relies on a random initialization and Lloyd's algorithm can get stuck in local optima of the k-means objective function, I have to run it several ...
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1answer
87 views

Validity Index Pseudo F for K-Means Clustering

The Validity Index "Pseudo F" is described as: (between-cluster-sum-of-squares / (c-1)) / (within-cluster-sum-of-squares / (n-c)) with c beeing the number of clusters and n beeing the number of ...
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138 views

Performance metrics to evaluate unsupervised learning

With respect to the unsupervised learning (like clustering), are there any metrics to evaluate performance?
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35 views

What is exactly code vector and quantization vector of self organizing map?

I am trying to understand code vector in self organizing map. Could anybody explain me intuitively what it is exactly?
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78 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 ...
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111 views

Use of a Poisson distribution from a distance matrix to determine dbscan parameters

I´ve been researching about automatic determination of parameters for DBSCAN (a density-based clustering algorithm -- http://en.wikipedia.org/wiki/DBSCAN), especially eps, and have found the following ...
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96 views

Use a clustering as a segmentation

I ran a cluster analysis on a population of customers. I used variables like: Lifetime Spent amount etc. Now I'd like to use these clusters ('Little buyer', 'Regular fan of the brand A', ...) for ...
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110 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 ...
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1answer
221 views

Clustering inertia formula in scikit learn

I would like to code a kmeans clustering in python using pandas and scikit learn. In order to select the good k, I would like to code the Gap Statistic from Tibshirani and al 2001 (pdf). I would like ...
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21 views

Could Robust Subspace Clustering be used to remove outliers?

I have two samples set, one is positive, and the other is negative. But, in each of both sets, there are some outliers that don't belong to it. Can the Robust subspace clustering be used to help me ...
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41 views

Clustering data for occurrence

I have a set of data representing nodes and how often they have been involved with each other. I've processed this into a table containing the nodes on X and Y with the data being the number of ...
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33 views

Pooled time series

Can somebody tell me what are and how to do pooled time series? and in what way it can help me to build forecast models from clusters of time series? Maharaj and Brett do this in their article ...
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280 views

Assign weights to variables in cluster analysis

I want to assign different weights to the variables in my cluster analysis, but my program (Stata) doesn't seem to have an option for this, so I need to do it manually. Imagine 4 variables A, B, C, ...
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71 views

Hierarchical clustering on defined labeled clusters

So I have these data points and I know the ground truth/labels of these points. I want to use Hierarchical clustering on the dataset given that all of the points that have the same labels are ...
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Is it necessary to split data in clustering like in supervised learning?

I'm learning clustering analysis and one book I read says the clustering model should be applied to a disjoint data set to examine the consistency of the model. I think in clustering analysis we ...
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109 views

Estimating most important features in a k-means cluster partition

Is there a way to determine which features/variables of the dataset are the most important/dominant within a kmeans cluster solution generated via R?
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99 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 ...
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126 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 ...
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114 views

Comparing clustering of sequences in datasets with different N?

When doing sequence analysis using a package such as TraMineR, one can calculate a clustering based on Optimal Matching (OM) distances, and then plot it as a tree. ...
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83 views

C-Index for cluster analysis in Stata

I'm wondering how to calculate the C-Index for determining a 'good' number of groups in a cluster analysis in Stata? It's mentioned in this post (What is an acceptable value of the Calinski & ...
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19 views

Choose canonical values from clusters of erroneous ones

I suspect this is a statistical problem, but it may be just an algorithmic one. So, more formally: Given: a set C of unknown ‘canonical' values an error window e a set V of known values, s.t. ...
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172 views

What is the intuition behind the variation of information (VI) metric against others 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 ...
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2answers
152 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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36 views

Analyze which items contributed the most to a significant anova simple effect

I have a generic question that I am not even sure how to formulate, but: Imagine that two categorical factors, A and B, that are well-known to interact. A is manipulated as a between-subject factor ...