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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48 views

How can an artificial neural network ANN, be used for unsupervised clustering?

I understand how an artificial neural network (ANN), can be trained in a supervised manner using backpropogation to improve the fitting by decreasing the error in ...
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2answers
72 views

Approach and example of graph clustering in “R”

I am looking to group/merge nodes in a graph using graph clustering in 'r'. Here is a stunningly toy variation of my problem. There are two "clusters" There is a "bridge" connecting the clusters ...
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5answers
9k views

Clustering with a distance matrix

I have a (symmetric) matrix M that represents the distance between each pair of nodes. For example, A B C D E F G H I J K L A 0 20 ...
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3answers
129 views

Comparison of close data sets

I'm studying around 100 sets of temperature ($N_{sample}=500$), which depends $4$ explicative variables such as power or speed. The dependency is always the same in each set, but sometimes the mean ...
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1answer
329 views

An incremental Gaussian mixture model

Question 1: Suppose that data is modelled by a mixture of K probability distributions which are actually Gaussians. $P(x_i|\theta_j)$ is the probability density of the j'th cluster, for which the ...
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1answer
15 views

Finding words belonging to a topic

Consider forum posts or any text where we'd be interested in finding out related words, given the data. What would be a solution for creating a topic cluster based on this data? E.g. We are interested ...
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0answers
11 views

Determining clustering words

I'm looking for an alternative to PMI for the following problem: I have a set of $n$ classes of text corpuses, and I'm trying to find the keywords that differentiate the corpuses from each other. For ...
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1answer
104 views

Evaluation measures of overlapping clustering

I have a dataset of Facebook users and a set of different clustering algorithms. The project goal is to draw up a rank between these algorithms in order to understand which of them are the good ones. ...
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1answer
232 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 ...
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0answers
6 views

How to make clustering / segmentation on date to percentage intervals?

I've got a table with 3 columns: query, ctr and mean position I want to identify all the cases where the CTR is low while the mean position is good. First of all I need to segment the continuous CTR ...
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0answers
19 views

How can we say that a clustering quality measure is good?

There are few well known measures like silhouette width (SW), the Davies- Bouldin index (DB), the Calinski-Harabasz index (CH), and the Dunn index . How can we say that a clustering quality measure ...
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1answer
221 views

Gaussian neighborhood function and non linear learning rate for self-organizing map 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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1answer
23 views

SOM dimension doubt

I'm currently working on a research of data clustering using an ANN for self-organizing maps. I'm performing experiments using Matlab, over a Dataset of 20,000 samples and almost 80 variables. The ...
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2answers
99 views

Analysing data on importance ratings

I had following question in my questionnaire: Rate the following factors: price, quality, advertisement, brand, reference from 1 (very important) to 5 (least important) that may have influenced your ...
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1answer
87 views

Detecting strong currents in a sparse directed graph

I have a very large, sparse, weighted, directed graph. The structure is such that it mainly consists of strings of nodes connected with highly weighted edges. These strings can be connected by weak ...
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1answer
31 views

What does it mean for Latent Dirichlet Allocation results to be “good”?

In most paper, Latent Dirichlet Allocation (LDA) model is used for clustering, and the value of $K$ is trained manually (e.g. http://astro.temple.edu/~tua95067/grbovic_cikm.pdf). They claim that this ...
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1answer
95 views

Motivations for Shi-Malik Algorithm

So I've been trying to make sense of the clustering algorithm on page 6 of this paper. Are the "first" k eigenvalues they refer to the smallest eigenvalues? What are the $y_i$ exactly? I don't ...
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1answer
45 views

Procedure for the cluster-robust Hausman test

The Hausman test cannot be run on robust std. errors we have separately make the FE and RE standard errors robust to serial correlation and heteroskedasticity by clustered standard errors. So, is ...
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0answers
29 views

Does my yeast population split?

Description of the data Imagine a population of yeasts that move along 1 dimension through time. They may move more or less randomly and eventually at some point the population will more or less ...
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2answers
1k views

Quantitative evaluation metric of kmeans clustering results

I'm using k-means to cluster sentences according to the part-of-speech tags of the words in a sentence, and I have a nice, easy to understand visualization of the result, but I'm struggling to find a ...
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1answer
15 views

k-means and other non-parametric methods for clustering 1 dimensional data

I know that a few people asked this question before and that clustering is not the best method for 1 dimensional data. However, I saw that in some published papers people used k-means clustering for 1 ...
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0answers
37 views

How to detect clustering or related anomalies in cross-section data

I have a cross-section of 100,000 individuals and information on their age. I suspect that there may be clustering by age or that the sample exhibits behavior that there would be two groups, the old ...
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1answer
415 views

Clustering text with python

I asked on stackoverflow but they suggest me to move here for better answers. I copy paste the question. I decide to play a little with similarities and clustering text. I have already create the ...
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1answer
17 views

Proof that points change clusters less often as iterations proceed in k means

Is there a way that to prove the following: In k-means clustering, as the iterations proceed, the data points tend to stay in their existing clusters, overall, because the replacement of the centroid ...
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0answers
79 views

Cluster or factor analysis?

I read a lot of forum to understand this, but I'm only more confused. I have a database with some comorbidities and I want to see if they could be divided in groups. I did a cluster analysis and a ...
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0answers
16 views

K-Medoids Clustering without dissimilarity matrix in R

I've been reading the documentation and some examples I've been able to find about k-medoids clustering but can't find a good answer anywhere (if it exists, apologies -- please just point me in the ...
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1answer
185 views

Determining similar users from hierarchical clustering

I use hierarchical clustering to cluster users which are similar to each other based on a Jaccard coefficient. I have now coded a solution to extract similar users based on hierarchical clustering: ...
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2answers
40 views

Clustering a correlation matrix

I have a correlation matrix which states how every item is correlated to the other item. Hence for a N items, I already have a N*N correlation matrix. Using this correlation matrix how do I cluster ...
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0answers
44 views

One dimensional clustering (again!)

I know this question has been asked a lot, but my problem is a lot more specific than those questions, and the solutions provided don't seem to apply. Here's the problem: I have a set of values ...
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1answer
518 views

What is the intuition behind the variation of information (VI) metric 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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0answers
29 views

Generalised Tobit for clustered data (type 2?)

I would like to make a Tobit estimation where my dependent variable is stadium attendance, but there are observations from 18 different stadiums (different capacities). My thoughts are it may be type ...
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2answers
128 views

Agreement of clustered data

I have the following situation: I have analyzed several data curves from a group of patients (16 curves per patient) with different analysis methods and want to test for the agreement of the methods. ...
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0answers
733 views

How to plot Optics Clustering result in Matlab (reachability plot)

I modified the following script for Optics clustering ( http://chemometria.us.edu.pl/download/OPTICS.M ) in order to work with DTW distance instead than Euclidean's. I obtained the Order vector ...
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1answer
21 views

Best Clustering Algorithm for Protein data

I have 400 virus genomes. In each virus, there are 100 genes (these are rough estimates). The genes in these viruses are transferred between each other very frequently. So Gene5 of Virus1 could be ...
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2answers
401 views

Clustering of points based on vector feature similarities in R

I have as an input a number of points that I need to partition into clusters. Each point has a number of features that are ideally to be used to find the similarity between each point and the others. ...
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0answers
93 views

Cluster prediction of incoming time series(partial)

I have a data set (24 x 1000) (hour x kwh) which contains 1000 time series of a buildings' power consumption, measured every hour. After applying k-means clustering using the dtw criterion I create 5 ...
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2answers
203 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 ...
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1answer
198 views

Quantitative results of clustering analysis

Currently, I am doing a clustering analysis for two sets of data. One smaller dataset (about 100 data) got ground truth labels, and one larger dataset (about 2000 data) has no ground truth labels. ...
2
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2answers
122 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 ...
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1answer
32 views

Best method to assign new customers to existing clusters after segmentation?

After segmenting customer base using k means algorithm into 5 clusters , how to assign a new customer to one of the existing 5 clusters? Matching just the mean of clusters with values of new ...
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0answers
14 views

Online clustering with distances

I'm pretty new to this field so please excuse me if my question sounds naive. I have a stream of distance tuples in the form of (A, B, d) where ...
0
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2answers
116 views

Proper Statistical Test for Binary Data

I looking for the best statistical test to apply in a particular situation and I hope I can find here the answer(s) I'm looking for. First of all some details: I'm studying 33 different mutants of a ...
2
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1answer
110 views

Finding the cluster centers in kernel k-means clustering

I think this is the most easily understood topic in Kernel K Means Clustering. But assuming that I am not an expert in Machine Learning, can someone tell me how does someone calculate Kernel K means ...
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1answer
716 views

How do I algorithmically determine values of T1 & T2 for canopy clustering?

I am trying to use canopy clustering to provide initial clusters for KMeans in mahout. Is there a way to determine / approximate the values of the distance thresholds T1 & T2 algorithmically? ...
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1answer
126 views

How to generate new Topic for new documents?

what approach would help me generate new topics for new documents? I read this page in order to learn more about the effect of specifying keywords for the topics that we care about detecting in new ...
1
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1answer
76 views

Clustering methods for unknown number of clusters

Matrix $X=[x_1,...,x_i,...,x_N]$ is a data-set containing $N$ data-points that each data-point $x_i$ is a vector of $D$ dimensions. Each dimension is a feature. The number of clusters ($K$) is ...
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0answers
12 views

Fuzzy Clusterization and rule etraction

I am trying to do Fuzzy Clusterization and fuzzy rule generation from weather data of different cities worldwide. The goal is cities to be clusterized by the type of climate they have ( tropical, ...
0
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1answer
11 views

Can a 1-D risk score (binary outcome) be sensibly used to create more than 2 treatment groups?

This question concerns predicted probabilities of a binary outcome, and the (I believe) misguided practice of making multiple cutpoints along a one-dimensional risk continuum -- cutpoints that create ...
3
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1answer
98 views

Dummy variables to control for clustering

I have a panel-data sample which is not too large (1,973 observations). The unit of analysis is x (credit cards), which is grouped by y (say, individuals owning different credit cards). I cannot used ...
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2answers
24 views

How to create 2 groups from 1

I have one large group of data and each row which pertains to one animal and its size. So per row I know the size of the animal, here is an example: ...