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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3 views

Reveal confusing class blocs in a large confusion matrix

I built a linear classifier for 85 classes. When I get predictions, I construct a confusion matrix. If I visualize it, it looks pretty noisy. I would like to reorder rows and columns such that I ...
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0answers
6 views

Unsupervised Clustering using randomForest

Outline of clustering technique using Random Forest A synthetic data is created by randomly sampling from the data of interest. It is used as the base line to measure the "structureness" or ...
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1answer
106 views

What is the difference between graphs/networks? [on hold]

Background: In a previous question I asked how to group what I would call nodes on a network graph based on a connectivity matrix. (link) The nomenclature used for a group of points was "cluster", ...
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1answer
15 views

interactive clustering of a 3D point cloud by changing the granularity

I want to cluster a point cloud in a 3D space (maybe with 200k points). For this I'm locking for a botom up approach. My goal is, that I can change the granularity of the clustering with a ...
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0answers
21 views

How to categorize users based on their movie views?

Apologies for cross posting. I have a dataset of size (61573, 25). The rows represent users whereas the columns represent ...
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2answers
117 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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12 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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0answers
8 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
22 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
18 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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2answers
86 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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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 ...
2
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0answers
31 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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1answer
16 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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1answer
32 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
25 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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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
41 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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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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0answers
46 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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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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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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0answers
94 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
44 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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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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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 ...
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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, ...
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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 ...
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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: ...
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1answer
99 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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1answer
16 views

Pick representative element from each cluster

I have made a hierarchical clustering and dived it into a distinct number of clusters. Now from each cluster I would like to pick one element representing the cluster best. What would be a good ...
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1answer
78 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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48 views

Hierarchical clustering, linkage methods and dynamic time warping

My goal is to cluster time series based on their DTW distance. Therefore I've calculated full distance matrices as input for several clustering algorithms. I first had a look at hierarchical methods, ...
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1answer
27 views

Adaptive Sampling Design

In a pdf, the definition of adaptive sampling design is written as : "An adaptive sampling design is one in which the selection of units to include in the sample depends on ...
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0answers
3 views

Gesture Recognition with HMM and Matlab [migrated]

I'm trying to classify some gestures with Matlab, using k-means and Hidden Markov Model. As example, I trained 10 samples of 'circle' hand gesture, organized in three .csv files where each columns ...
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1answer
47 views

K-means initial centers membership

I'm trying to plot all the steps of a k-means algorithm with r, but I can't. The k-means algorithm works in this way: Step 1. Initialize the center of the clusters Step 2. Assign the closest ...
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2answers
71 views

Clustering based on large Jensen-Shannon Divergence distance matrix

I have a dataset with large number of features and about 15 000 observations. I’m using a probability distribution distance metric related to Jensen-Shannon divergence (JSD) to cluster the ...
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3answers
85 views

Ask for suggestions on clustering methods on a large dataset with mixed types of variables

I need to build segmentation on a large customer dataset with more than 300K records and many variables, including continuous like income and age, ordinal like education level and membership level, ...
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1answer
46 views

Hierarchical Dirichlet Processes in topic modeling

I think I understand the main ideas of hierarchical dirichlet processes, but I don't understand the specifics of its application in topic modeling. Basically, the idea is that we have the following ...
5
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1answer
129 views

k-means|| a.k.a. Scalable K-Means++

Bahman Bahmani et al. introduced k-means||, which is a faster version of k-means++. This algorithm is taken from page 4 of their paper, Bahmani, B., Moseley, B., Vattani, A., Kumar, R., & ...
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2answers
53 views

When make clusters in a predictive glm model?

If I want to build a predictive glm model, should I make cluster analysis on 100% of observations or on training sample (80%)? Thanks
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0answers
21 views

Cluster Analysis in splitted database framework

I hope you can help me about my question. I want to build a predictive model, but as first step I need to define some clusters. I split my dataset in two subsets: 80% of observations in the training ...
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1answer
33 views

technical issues regarding to cluster analysis

Hi I would like to seek help with my cluster analysis using SAS. The main objective of the task is to segment customers into groups based on their similarity. The dataset contain mixed types of ...
5
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2answers
152 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 ...
4
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4answers
140 views

Is the triangle inequality fulfilled for standard hierarchical clustering distances?

For hierarchical clustering I often see the following two "metrics" (they aren't exactly speaking) for measuring the distance between two random variables $X$ and $Y$: ...
3
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1answer
64 views

Mclust function of mclust package overfitting Gaussians

I'm using the Mclust function of the mclust package in R to fit a mixture of Gaussians model. My simulated data obviously has 3 ...
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1answer
55 views
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24 views

Determining k in k-means clustering by community detection in graph

I am faced with a problem of choosing an appropriate number of clusters in highly dimensional data. I've read many approaches to determine the number of clusters, and finally came to a solution and I ...
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0answers
38 views

Structural Stability of Hierarchical Clustering

I am interested in some papers and reports about analysing the following problem: Assume, we have a stream of objects and a defined similarity/distance measure to calculate similarity/distance between ...
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2answers
45 views

Does clustering need scalar data?

I am trying to cluster 43,000 individuals on about 50 variables. The data contained in the variables are minutes of a radio shows which people listened to in the range of 0 - 3,000,000 minutes. My ...