Cluster analysis is the task of 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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Use clustering to create labels of unlabeled data and then classify a test set (available or not in the clustering)?

Let's say that I use Dynamic Time Warping (DTW) along with K-Medoids to cluster unlabeled time-series into a number of clusters. In this way, several clustering solutions in $k_i,i=[1,...,m]$ clusters ...
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2answers
107 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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1answer
258 views

jenks natural breaks vs k-means

I am new to this topic. As far as I know both are data clustering methods. Then my question is when is Jenks prefered over k-means? I read on this website that jenks is particularly suited for ...
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23 views

seek help on clustering analysis

I have about 79,000 game players' data and we are trying to cluster these players into different classes. But so far we did not get a consistent cluster solution (we used K-means clustering). I ...
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3answers
26 views

Why choosing proper initial centroids is very important for K-means?

I don't fully understand why choosing proper initial centroids is very important for K-means. Demos or simple explanations will be very grateful. Thank you !
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1answer
333 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 ...
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1answer
16 views

trouble in understanding outliers' influence on K-means

When outliers are present, the resulting cluster centroids may not be as representative as they otherwise would be and thus, the SSE will be higher as well. However, I don't understand this ...
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0answers
11 views

How to cluster sets (users/documents) with distributed MinHash using the banding technique? [on hold]

I have a big doubt about the way I should cluster sets using MinHash together with the banding technique. I assume everyone reading has a good knowledge of MinHash so I won't define most of the terms ...
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0answers
9 views

What are appropriate clustering algorithms for very sparse data with a large number of binary features? [duplicate]

I have a dataset reporting the courses taken by approximately 100k students over a 2 year period. I'd like to cluster these students based on the courses they took. I’ve organized the data so that ...
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1answer
289 views

How to conduct community division of a social network with R?

I am trying to use R to conduct community division within my weighted network (based from an association matrix). I tried with igraph but I encountered some problems. I usually use the program Socprog ...
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0answers
5 views

Rule clustering/generalization based on generated records

I am looking for paper related to the rule clustering/generalization problem. I have a set of rules and corpus of files, that behave in the following way: Rules are search smart regex patterns ...
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1answer
18 views

SOM based on a not euclidean distance

Suppose one has trained a SOM on a certain number of data. Without explaining all the procedure, one can say that the SOM algorithm produces a certain number of prototypes and the new elements coming ...
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2answers
158 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
16 views

How can I use clustering algorithms to bin highly skewed data process?

I have a large set of multi dimensional data.The data points are highly skewed and not smoothly distributed.I want to divide the data set to some finite number of bins.I have approached this problem ...
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0answers
150 views

Multiple eigenvectors in graph spectral clustering

In Newman's PNAS 2006 paper Modularity and community structure in networks, the first eigenvector splits the graph in two clusters, and then each cluster can be further divided by eigenvector of a ...
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0answers
8 views

Implementation of Meila's VI criterion in Python? [on hold]

I am doing some clustering experiments and came across this paper by Marina Meilă in the Journal of Multivar Statistics, where she presents a very interesting metric for evaluating clusterings called ...
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1answer
1k views

Clustering text with python

I have asked on StackOverflow, but they suggested me to move here for better answers. I copy paste the question. I have decided to play a little with similarities and clustering text. I have already ...
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2answers
462 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
22 views

If I use PCA before clustering, do I need to use PCA scores on new axes(principal components) to run clustering?

I want to use PCA before clustering, and then I want to run a clustering algorithm such as K-Means. My understanding is that I run PCA and find loadings for each original variable, then calculate ...
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3answers
10k views

Reason to normalize in euclidean distance measures in hierarchical clustering

Apparently, in hierarchical clustering in which the distance measure is Euclidean distance, the data must be first normalized or standardized to prevent the covariate with the highest variance from ...
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1answer
146 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 ...
3
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1answer
364 views

Quantitative results of cluster analysis

Currently, I am doing a clustering 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. For the ...
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1answer
2k views

Interpreting output of igraph's fastgreedy.community clustering method

With the help of several people in this community I have been wetting my feet in clustering some social network data using igraph's implementation of modularity-based clustering. I am having some ...
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21 views

Feature ranking for *known* clusters

I am aware of feature ranking (i.e. selecting the 'best' features) for a binary classification task based on some model, however, I was wondering how to do this in the absence of a model? For example, ...
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8 views

Need help to classify products using R [closed]

I have an ecommerce data set with following columns: Product ID Product Description Search query terms Based on the product description I should be able to classify it into correct categories eg, ...
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1answer
400 views

The best way for clustering an adjacency matrix

I've had a hard time interpreting resulting clusters of an adjacency matrix. I have 200 relatively big matrices representing subjects that contains partial correlations (z scores) of time series ...
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2answers
13k views

Interpreting result of k-means clustering in R

I was using the kmeans instruction of R for performing the k-means algorithm on Anderson's iris dataset. I have a question about some parameters that I got. The ...
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1answer
30 views

Can cluster analysis of preclassified items gives idea about the classification performance?

Suppose in a classification we have a dataset with many features and their class, we want to select some features using which we can construct a classifier. We perform the cluster evaluation for the ...
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Can somebody set an exmaple of the steps for doing K-means with PCA below? [closed]

I have found a paper on the Internet and have read it. But there are some steps which are not really clear to me. If you already understand, help me understand what those steps say. Input: X={d1, d2, ...
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2answers
772 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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1answer
128 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 ...
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1answer
2k 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 ...
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0answers
14 views

How to interpret the results of clustering on text documents

I am working on Text Analysis of the Feedback's given in a Survey. I wanted to identify the different themes or topics people are talking about. So, i have desired to go ahead and do Clustering. ...
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1answer
66 views

SOM (Kohonen) using the term document matrix [closed]

Language: R Package: kohonen Function: som I have a term document matrix (tdm) with 64 terms (row) and 1017 documents (columns). I want to use the self-organized-map to cluster the terms on 25 ...
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11 views

what would be an appropriate clustering method in my case?

the data has several hundreds of dimensions, each dimension is within range (-1, 1) points in a cluster follow some Gaussian distribution distance measure can be Euclidean or Mahalanobis the ...
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31 views

Cluster vs factor analysis for grouping variables and cases

I've noticed responses that at face value seem to be in contradiction with each other. For instance, here @peter-flom writes Short answer: Cluster analysis is about grouping subjects (e.g. ...
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2answers
28 views

Is support vector clustering a method for implementing k-means, or is it a different clustering algorithm?

The question is in the title. But I would also like to know, if it is different, what is the essence of the difference?
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1answer
20 views

Can I use Yule's distance metric for continuous data?

I've been building a clustering modelling for my large data set (3700 x 891). When I thought of picking appropriate distance metric, I've decided to compare all the distance metrics in scipy module ...
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2answers
1k views

Does Newman's network modularity work for signed, weighted graphs?

The modularity of a graph is defined on its Wikipedia page. In a different post, somebody explained that modularity can easily be computed (and maximized) for weighted networks because the adjacency ...
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7answers
13k views

How to do community detection in a weighted social network/graph?

I'm wondering if someone could suggest what are good starting points when it comes to performing community detection/graph partitioning/clustering on a graph that has weighted, undirected edges. The ...
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3answers
160 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
28 views

Testing whether two datasets cluster similarly

Most questions about cluster analysis seem to come from people who have a single dataset and want to compare/quantify the similarity of different clustering approaches. This question is not that. ...
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1answer
143 views

Random forest clustering

In my data the classes were defined by binning a variable in 10 bins. After growing the random forest its proximity matrix is viewed as the following MDSplot: As can be seen from the plot all ...
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2answers
145 views

Three-dimensional phylogenetic tree “anchored” in a scatter plot

I have a phylogenetic tree and data on two traits, x and y. To present them, I would like to show the phylogenetic relationships while preserving the xy positions in trait space (i.e., convex hulls). ...
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3answers
5k views

How to perform K-medoids when having the distance matrix

I've been trying for a long time to figure out how to perform (on paper)the K-medoids algorithm, however I'm not able to understand how to begin and iterate. for example: I have the distance matrix ...
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2answers
2k views

Newman's modularity clustering for graphs

I am interested in running Newman's modularity clustering algorithm on a large graph. If you can point me to a library (or R package, etc) that implements it I would be most grateful.
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2answers
170 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 ...
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0answers
20 views

Detecting sustained increases in employee pay (time-series data, non-stationary)

I am seeking guidance with detecting features (specifically, sustained pay-rises) in monthly income data. I haven’t worked much in the time series space so nothing straightforward springs to mind ...
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1answer
17 views

Does Newman clustering work on weighted graph with non-integer weights?

I have a weighted undirected graph, where weight is distance and it is between 0 and 1. I want to apply the weighted version of Newman clustering. I think weight must refer to strength or similarity, ...
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1answer
36 views

When can clustering be used for dimensionality reduction? [closed]

Can a clustering method be used for dimensionality reduction? I though the answer would be that the cluster numbers can act as the synthetic reduced dimension -- but the other day a friend had a more ...