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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2
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2answers
129 views

Relevance of overall absolute values in covariance analysis of two variables

I am performing K means clustering on a gene expression dataset. I am aware of the fact that the Pearson correlation metric allows to group trends or patterns irrespective of their overall level of ...
2
votes
0answers
15 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 ...
5
votes
1answer
103 views

Co-occurrence of properties in a population

I have 150 properties that may occur in a population of 10000 people. Individual people may have none, one or a couple of these properties. The properties are not mutually exclusive and have different ...
0
votes
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 ...
4
votes
1answer
280 views

Clustering data that has mixture of continuous and categorical variables

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 ...
0
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0answers
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 ...
0
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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 / ...
1
vote
1answer
51 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 ...
1
vote
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. ...
4
votes
3answers
666 views

How to explain how I divided a bimodal distribution based on kernel density estimation

I have a dataset of bimodal population. It contains a smaller peak, which is considered to be "bad", and a bigger peak. I try to separate the bad part of data from the rest of data. What I did was: ...
-2
votes
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 ...
2
votes
0answers
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 ...
3
votes
1answer
308 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 ...
6
votes
1answer
218 views

Clustering without a distance matrix

I've recently completed a project where I used scikit-learn's DBSCAN module to find clusters in relatively short strings of text. I used a custom string similarity ...
0
votes
0answers
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 ...
1
vote
1answer
621 views

Multidimensional scaling using Python

I have 6,000 points for which I have all pairwise distances in a distance matrix. I want to get an idea whether these data were generated by a mixture of Gaussian distributions so I'm trying to get a ...
1
vote
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 ...
7
votes
1answer
1k views

Clustering probability distributions - methods & metrics?

I have some data points, each containing 5 vectors of agglomerated discrete results, each vector's results generated by a different distribution, (the specific kind of which I am not sure, my best ...
1
vote
2answers
555 views

Clustering a long list of strings (words) into similarity groups

I have the following problem at hand: I have a very long list of words, possibly names, surnames, etc. I need to cluster this word list, such that similar words, for example words with similar edit ...
5
votes
4answers
6k views

Is cosine similarity a classification or a clustering technique?

In document classification, is cosine similarity considered a classification or a clustering technique? But you need training data with the cosine similarity for creation of the centroid right?
0
votes
0answers
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 ...
4
votes
1answer
89 views

Good (2d) visualization of a mixture model clustering

I have a specific problem which I'm surprised I don't find answers on-line and I hope somebody here has a good suggestion for me. I'm working with a large data set which I'm clustering into specific ...
0
votes
0answers
15 views
0
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0answers
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 ...
0
votes
0answers
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 ...
5
votes
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 ...
7
votes
4answers
991 views

Latent Class Analysis vs. Cluster Analysis - differences in inferences?

What are the differences in inferences that can be made from a latent class analysis (LCA) versus a cluster analysis? Is it correct that a LCA assumes an underlying latent variable that gives rise to ...
0
votes
1answer
481 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 - ...
2
votes
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.
0
votes
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 ...
0
votes
1answer
148 views

How to use both binary and continous variables together in K means/Hierarchical clustering in SAS/R?

I need to use binary variables( values 0 & 1) in K means. But K means works with only continuous variables. I know some people still use these binary variables in K means ignoring the fact that k ...
1
vote
1answer
106 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 ...
1
vote
1answer
115 views

Document image analysis and retrieval with online incremental clustering

Is there any interesting problem in the area of "Document Image Analysis and Retrieval" which by nature needs an online/incremental clustering process ? The problem may be in the context of "Logical ...
0
votes
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 ...
1
vote
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 ...
2
votes
2answers
38 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 ...
0
votes
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 ...
0
votes
0answers
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?
1
vote
0answers
10 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, ...
1
vote
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 ...
3
votes
1answer
219 views

Distance between two Gaussian mixtures to evaluate cluster solutions

I'm running a quick simulation to compare different clustering methods, and currently hit a snag trying to evaluate the cluster solutions. I know of various validation metrics (many found in ...
0
votes
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.
1
vote
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 ...
1
vote
1answer
36 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 ...
0
votes
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 ...
0
votes
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: ...
0
votes
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 ...
0
votes
2answers
96 views

Bisecting K-means using Dynamic Time Warping

I'm trying to cluster time series of different length and I came up to an idea to use DTW as a similarity measure, which seems to be adequate, but the thing is, I cannot use it with K-means, since ...
5
votes
2answers
197 views

Dirichlet Processes for clustering: how to deal with labels?

Q: What is the standard way to cluster data using a Dirichlet Process? When using Gibbs sampling clusters appear and dissapear during the sampling. Besides, we have a identifiability problem since ...
0
votes
0answers
39 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 ...