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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8 views

How to specify K cluster in Hierarchical clustering with noisy data?

I'm new in Mining and Clustering and I wonder how to cut off the hierarchical clustering Dendrogram to obtain a specific number of clusters. The problem is here that the data is noisy and the ...
2
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0answers
16 views

Observations get in a line in a PCA score plot. Something wrong with the data?

I ran a clustering and in the resultant PCA score plot some observations getting in a line drew my attention (I marked them with a red line) . How come they distribute like that? I doubt there is ...
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0answers
12 views

Different Methods for clustering skills in text

Consider a talent pool in which each member has some set of skills. Some of these talent are submitted to orders as potential candidates of which one is selected. It is reasonable to assume that the ...
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29 views

Feature extraction based on correlations

I have a small problem regarding feature extraction with correlation. I have divided my question in four parts hoping that somebody can help me. I have a dataset consisting of fMRI images. Each image ...
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1answer
22 views

Correlation / clustering over lognormal data

I'm working with some financial data and it turns out my data is pretty much lognormal distributed. The question I have is, which produces "better" results: using plain data to find correlation / ...
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1answer
12 views

Is there a version of Latent Class Analysis with unspecified # of clusters

I understand that you can use the elbow method to plot LCA solutions vs log likelihood to figure out, at which k, it is no longer worth it to add more clusters. And I will resort to this if need be. ...
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1answer
52 views

Unknown writing system: different letters or variants of the same letter?

In a fictitious language, there are 4 graphic variants of what is commonly believed to be the same letter "a": a1, a2, a3, a4. In a corpus of texts, any word containing "a" (Xa, Ya, Za, etc.) can be ...
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6 views

Disadvantages of cluster randomised controlled trials

Can anyone explain some of the disadvantages of a cluster randomized controlled trial? I read something about data being more correlation between partiticipnts in each condition/group but I don't ...
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1answer
20 views

How to do text clustering for a set of around 10000 messages?

I have around 10000 messages in a variable, i want to form clusters of them based on similarity, so that I can assign some class say 1-10, if 10 clusters are formed and run analysis on them. How can ...
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12 views

Trying to understand xmeans (using R, RWeka)

In a project I want to use XMeans to estimate the 'optimal' number of clusters that are distinguishable in different datasets. The numbers I got seemed too low, so I experimented a bit with generated ...
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0answers
16 views

R: How to choose the height parameter in cutree, or: how to find the optimal number of clusters in UPGMA clustering?

I am using hclust() to carry out a UPGMA clustering (method="average") in R. Then, I'm using ...
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13 views

Order of cases in clustering methods [closed]

When I run a hierarchical cluster analysis with only ordinal binary variables (asymmetric categories: present vs absent), the output (e.g. the assignment of cases to clusters) is dependent on how my ...
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47 views
+50

Incorporate new unlabeled data into classifier trained on a small set of labeled data

I have a set of 400 labeled samples (8 numeric features) on which I trained a binary classifier. The problem I am facing is that once the classifier is shipped to the users, I will get additional ...
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28 views

Which unsupervised learning method should I use on classification on many point cloud datasets?

I have a few abstract and high dimensional point clouds in the form of distance matrices. I want to do unsupervised learning on this dataset. The problem is, I am not using one distance matrix, but ...
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2answers
322 views

Do I need to remove duplicates for cluster analysis?

I am doing a cluster analyis and I was wondering whether it is possible to remove duplicates from the data set - in order to increase performance. I work on tables where objects are in rows and ...
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0answers
16 views

Integrated Classification Likelihood computation for R package HDclassif

I'm in the process of fitting some mixture models to some data I have. As this data is high-dimensional, I used the subspace clustering package HDclassif. As the package has no option for the Akaike ...
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3answers
53 views

Any easy way to cluster GPS trajectories?

Can anyone recommend an easy way to cluster hundreds of GPS trajectories to find out their common paths? The GPS data is coming from different vehicles that have traveled thousands of miles.
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2answers
45 views

Simple way for histograms Clustering

I'm trying to cluster set of histograms. The histograms represent the frequencies of the distribution for a numbers from 1 to 5. The following figure shows two samples of my data. I have 10,000 ...
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0answers
45 views

Rescaling Features for ML

I have data that is collected every month and I want to perform K-means clustering on each month (both on historical data and on future data). However, it isn't clear to me how best to rescale my data ...
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11 views

Standard deviation comparison for splitting clusters in ISODATA

I am currently implementing the ISODATA algorithm and I am new to cluster analysis as I just learnt about it. I got stuck at the step which I need to compute the standard deviation of each cluster, ...
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22 views

How to evaluate and compare two clustering algorithms in R for text mining

I am doing research in R language for text mining. I would like to know how to evaluate and compare two clustering algorithms in R for text mining?
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1answer
22 views

Hierarchical clustering with categorical variables - what distance/similarity to use in R? [duplicate]

I have only categorical variables in my database. What distance/similarity to use? I´m using the function simil() (library(proxy) in R.
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1answer
45 views

The most popular hierarchical clustering algorithm (divisive scheme)

My question: what is a "standard divisive hierarchical clustering algorithm". I have a well-defined similarity matrix, and have already carried out a clustering (with spectral + genetic clustering ...
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2answers
29 views

Clustering based on distance matrices

Given a pre-computed distance matrix, obtained from arbitrary samples, such as graphs, I am currently looking for efficient clustering algorithms to deal with distance matrices, so that the algorithm ...
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1answer
37 views

Observations from two distribution functions mixed, how to separate them?

Assume I have 100 observations, I know they are from two distribution functions, they are mixed together. Is this possible to find out which distribution they are coming from? Here is an example in ...
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0answers
11 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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0answers
11 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
40 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
32 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
41 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
12 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
28 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
17 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
40 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
21 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
27 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
35 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
27 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
14 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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25 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
40 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
38 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
46 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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17 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
101 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
24 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
20 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 ...
6
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1answer
96 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
12 views

Split graph into non-overlapping cliques [migrated]

I have a problem where I need to split a graph into subgraphs. The conditions for the splitting is as follows: Every subgraph must be a complete graph/clique No vertex can be part of two or more ...
2
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0answers
18 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 ...