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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24 views

comparing count data with a vector of quantitative Scores

I am working on a RNA-Seq data set from mouse. I have done the mapping and the counting and got a table of count data (a matrix of counts for each gene and sample), which looks like that: ...
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12 views

Quantization of an array of real values

I have an array of real values (~500K) that I would like to quantize/cluster. Looking at the histogram I can come up with a number of cluster centers but I prefer a data-driven approach. The values ...
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0answers
102 views

Hierarchical clustering of categorical variables in R - alternative algorithms / tools

I am running a hierarchical clustering process in R, using daisyto compute a dissimilarity matrix and ...
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1answer
199 views

R: visualizing kmodes clusters

I am working on cluster analysis of a completely categorical data set using package klaR and function kmodes. Sample of the ...
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1answer
72 views

Difference between PCA and spectral clustering for a small sample set of Boolean features

I have a dataset of 50 samples. Each sample is composed of 11 (possibly correlated) Boolean features. I would like to some how visualize these samples on a 2D plot and examine if there are ...
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0answers
14 views

Appropriate statistical test for Analysis of Clustered data

In cluster randomized control trial I used GEE for analysis. I found that because the number of cluster in my research is 8, then GEE is not appropriate and also I couldn't get the cluster effect in ...
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1answer
40 views

The design effect

The design effect (deff) quantifies the extent to which the expected sampling error in a survey departs from the sampling error that can be expected under simple random sampling . My question is ...
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0answers
29 views

Spectral Clustering: Laplacian vs Normalised Laplacian

I was looking at spectral clustering a graph. On looking at the Laplacian obtained, $L$ there does seem to be $5$ zero eigenvalues (rather eigenvalues close to 0 (i.e. $<0.01$)) and the sixth ...
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2answers
134 views

What is the best algorithm to find similar text documents?

I have many text documents and I would like to find similar documents to each document within my data set. Is Latent Dirichlet Allocation (LDA) the best way to do that, or are there other algorithms ...
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0answers
46 views

Feature selection in clustering

I am looking for a method for feature selection in Gaussian Mixture Models. I have a dataset with 2000 records and 40 variables. I tried to use the "clustvarsel" package in R, which use the BIC as ...
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1answer
29 views

Fast partitional clustering algorithm

I have a set of $N$ objects for which I can calculate the distance between each pair, so I can compute the distance matrix. However, establishing a distance between a pair of objects is not ...
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0answers
74 views

Variable importance using cforest in clustering / unsupervised learning application

I have a data set which I'd like to cluster by using random forest. As I have more than 50 variables, I first want to identify the most important features and subsequently cluster the data set based ...
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1answer
34 views

Regularizing soft kmeans with entropy

So in classical fuzzy k-means clustering, the objective function is $\sum_i \sum_j u_{ij} \|x_i - c_j\|^2$ Now, we want to regularize this objective function using the entropy: $\sum_i^n H(U_i) = - ...
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1answer
32 views

Adjusted Rand index - more clusters than categories in response variable

I ran clustering analysis for different k values - different numbers of clusters in R. Now I want to evaluate success with Adjusted Rand Index. However, my response variable has only 2 categories. So ...
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52 views

Cluster analysis in SPSS

I started learning cluster analysis (using SPSS) and I need some help in a practical problem. Given the following variables: The respondents were asked to indicate the importance of the ...
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0answers
106 views

Validate dendrogram in cluster analysis: What is the meaning of cophenetic correlation coefficient?

I want to calculate the cophenetic correlation coefficient. reading previous posts Comparison of cophenetic correlation coefficients on different data sets On cophenetic correlation for dendrogram ...
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1answer
69 views

Which (dis)similarity index to choose for cluster analysis?

I have data that refer to the number of occurrences of specific variable in samples: ...
3
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1answer
76 views

When should I use k-means instead of Spectral Clustering?

From the image linked to below, it looks like when the data actually consists of K isotropic clusters, Spectral Clustering does as well as K-means. But for other, non-convex clusters, Spectral ...
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0answers
30 views

Normalization of scale in cluster analysis

I have 16 variables which are scaled 1-5, 5 variable scaled 1-4 and 1 variable scaled 1-10. I suppose I will need to do normalization before applying cluster analysis. Variable response is in likert ...
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2answers
122 views

Treating missing data in voting pattern analysis

I'm trying to analyze voting patterns of Ukraine's parliament deputies. I scraped all the data on their voting during last session. Each data entry has following information: Deputy name, date, bill ...
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1answer
27 views

Absolute criterion for clustering

everyone. I am puzzled, when without having truth labels, is there exist an absolute measure for clustering, like correctness for classification, to evaluate the quality of a clustering result? That ...
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1answer
25 views

Feature representation for feature set clustering

I'm studying customer requirements clustering. Each customer's requirements are collected as a set of application features. I'd like to cluster those set of features, so that I can know what are the ...
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1answer
224 views

Efficient way to compute distances between centroids from distance matrix

Let us have square symmetric matrix of squared euclidean distances $\bf D$ between $n$ points and vector lengthed $n$ indicating cluster or group membership ($k$ clusters) of the points; a cluster may ...
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0answers
22 views

Similarity Measure for small strings

I am looking for a good similarity measure to conflate entries of a column (Product Brand in my scenario) Text like : ("Dell", "Dell Laptops"), ("ACP","ACP by XYZ"),("Acer Notebooks","Acer ...
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2answers
150 views

How to find similar documents in a big data set

I have many text text documents and my goal is to find similar documents. Apparently it is a clustering type of question and LDA (Latent Dirichlet Allocation) is a good candidate to do that. However ...
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2answers
54 views

Interpreting kmeans output

I am working on a clustering model with the kmeans() function in the package stats and I have a question about the output. My data is a sample from several tech companies and AAPL._UP is a variable ...
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1answer
56 views

Lagrange Multiplier for fuzzy clustering with size constrains

I'm trying to solve a clustering problem with size constrains. Minimize $J=\sum_{i=1}^c\sum_{j=1}^n {{u_i}_j}^2{d_i}_j$ $d_{ij}$ is the distance from each element to it's cluster center. Usually ...
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1answer
52 views

How to measure loss of performance of clustering by applying dimensionality reduction

Let's suppose I have a given dataset with $n$ features. Having a data-centric approach, I would like to measure the loss of performance of applying a given dimensionnality reduction technique, for a ...
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1answer
112 views

k means with binary variables

Is it OK to use kmeans with binary variables? I mean Euclidean distance? I guess the binary variables will be the ones that get the most power to determine the ...
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2answers
77 views

How to find weights for a dissimiliarity measure

I want to learn (deduce) attribute weights for my dissimilarity measure that I can use for clustering. I have some examples $(a_i,b_i)$ of pairs of objects that are "similar" (should be in the same ...
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2answers
76 views

Clustering from similarity/distance matrix [duplicate]

I have a symmetric and weighted adjacency matrix with $n$ elements. What algorithms exist to cluster the elements from this matrix? The matrix has values between $0$ and $1$. In the case of a ...
2
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1answer
29 views

Why do clustering internal validation indices are decreasing with the number of clusters?

I get the same pattern for 3 different indices: Silhouettes, Dunn and Connectivity- as the number of clusters increases, the score decreases. I am using several clustering methods and several distance ...
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0answers
44 views

how to perform divisive hierarchical clustering

I've been trying for a long time to figure out how to perform (on paper) the divisive hierarchical clustering algorithem, however I'm not able to understand how to do it exactly. example: I need to ...
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0answers
36 views

R: hclust() calculating variance within each cluster

I've got a distmatrix where the element at [i,j] describes the cosine distance between variable i and variable j. When I use hclust(), and then I use cut.tree to make K clusters, then I would like to ...
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0answers
26 views

An IR evalualuation metric that only measures the rank of results?

I am working on a little text clustering problem, and trying to figure out how to evaluate the results. I came up with the following idea that I though fits pretty well with the specifics of the ...
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0answers
19 views

Devising a mixed strength cost function for clustering

I'm asking this question with a Computer Vision background (my stat background is limited). I have a set of data that measure the edge strength (based on color gradient) of a set of colors. Since ...
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0answers
37 views

Interpret Kernel Density Estimation for Clustering

I would like to use KDE to cluster 1 dimensional data. For KDE I'm using the code published in MatlabWork ...
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0answers
16 views

Stability of Time Series Hierarchical Clustering

We have a dataset with six time points and three biological replicates each. Therefore, we have a vector of 18 measurements for each feature, and used hierarchical clustering with Euclidean distance ...
2
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2answers
49 views

Need a little help understanding K-means++ seeding

I have been working on a project that involves using K-means clustering for generating adaptive palettes from images. I understand the general process of K-means clustering, and I understand the ...
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1answer
39 views

How to check if the data is intermittent or too many zeros are due to seasonality?

I have a dataset for weekly number of calls to a call center for three years.The data is seasonal (I know this from practitioners knowledge) which means that calls normally come on summer and winter. ...
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3answers
77 views

Can you run clustering algorithms on perfectly collinear data?

Let's say I have the data set $x_i,y_i,z_i$, where $z_i=y_i-x_i$ or $z_i=f(x_i,y_i)$. Can I run clustering algorithms on this data set? I wanted to add non-linear or linear combinations of variables ...
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0answers
25 views

Clustering data with one feature

Is there any built in method to cluster data with one categorical dimension in R? Basically, I have a data set including week of the year and if an event happened in that week. I wanted to use ...
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1answer
84 views

What are the benefits for semi-supervised learning over unsupervised clustering? Or any limitations?

I have another question about semi-supervised learning vs unsupervised clustering, what are the benefits and limitations? I have got some data with labels and some without labels. I performed ...
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2answers
30 views

Line that separates data partitioned by the first principal component of PCA

I want to partition some 2d points into 2 groups (clustering). The way that I need to do it is by using PCA to find the first principle component. Then I project the data to find 1d projections. Then ...
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1answer
37 views

Clustering data points based on edge strength

I'm looking at a Computer Vision application where I try to analyze the strength of edges a certain set of colors make with another color. For, this I take images of two colors falling on top of each ...
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1answer
61 views

A proof of total sum of squares being equal to within-cluster sum of squares and between cluster sum of squares? [duplicate]

In cluster analysis I have frequently encountered a statement that the total sum of squares $\sum\limits_{i = 1}^n {{{({x_i} - \overline x )}^2}} $ being equal to within-cluster sum of squares ...
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0answers
26 views

How to estimate the the optimal number of clusters in a dataset by Clustering quality measures? [duplicate]

How to estimate the the optimal number of clusters in a dataset by Clustering quality measures? I have four datasets iris,breast cancer, magic ,wine and yeast. All the datasets are taken from UCI ...
1
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1answer
22 views

Can I do cluster analysis of dyadic data?

I have multilevel data that is dyadic in the unit of observation. The dyad is a unique pair of countries that sign a treaty, such that no dyad repeats itself. For example, the US-UK treaty, the ...
0
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1answer
57 views

Chosing optimal k and optimal distance-metric for k-means [duplicate]

I have a data-set with roughly 20-dimensions and millions of points which I want to cluster. The goal is to find a set of clusters which: Are as distinct as possible from each other (minimum ...
0
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0answers
37 views

Likelihood ratio test to choose between components of gaussian mixture model?

I have a Gaussian Mixture Model with 2 components. Is it possible to use a likelihood ratio test to determine the point at which the probability of being in component A is the same as being in ...