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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extern indeces with R [on hold]

with python there is predefined functions of extern indices( like jaccard, hamming, accuracy)in the package sklearn.metric, to evaluate and compare between ground truth and the clustering result. is ...
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6 views

Alternative to QDA and LDA on supervised classification

I have to do a supervised classification on a dataset made by 12 variables and 5 groups. The main problem is that my data are not gaussian so, i cannot make the quadratic and linear discriminant ...
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2answers
24 views

k means clustering for larger text fields

I'm a beginner in data science/machine learning and am attempting to work through some problems on my own I am running a K-means clustering on a dataset consisting of "mission statements". These can ...
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15 views

Alternative to self-organizing maps for image clustering [on hold]

I'm thinking to use self-organizing maps (with TensorFlow) to group images of people by some characteristics (like the color of their clothes). Any experiences with SOM on such task or do you suggest ...
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10 views

Dynamic Bag of Words / Features

I'm trying to implement a Bag of Features for a set of images submitted in different moments by a set of users. If the clusters change, then we need to recompute at LEAST all the "visual words" which ...
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19 views

Statistical measure for tf.Idf weight in document

I have 100 text document with different content size. I would like to label each document using the tf.idf weight. I have calculated tf.idf for the terms in each document. I plan to give the ...
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1answer
17 views

How many clusters I should choose using spectral clustering? [duplicate]

I tried to cluster my data using spectral clustering algorithm. Before applying clustering algorithm, I used PCA on the data, which gave me 4 PC accounting for 95% of variation. After that I plotted ...
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9 views

Bag of Features / Visual Words + Locality Sensitive Hashing

PREMISE: I'm really new to Computer Vision/Image Processing and Machine Learning (luckily, I'm more expert on Information retrieval), so please be kind with this filthy peasant! :D MY APPLICATION: ...
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1answer
24 views

What are some clustering algorithms in which I can define no of clusters I require?

Is there some other clustering algorithms apart from K-means in which I can define no of clusters I require ?I have a data set of large and skewed data points and K-Means is not providing quite ...
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1answer
18 views

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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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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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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3answers
27 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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13 views

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

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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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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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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1answer
21 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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10 views

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

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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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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1answer
23 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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1answer
32 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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16 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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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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33 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
29 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
21 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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1answer
30 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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22 views

Overlapping clustering

It looks like "overlapping clustering" scheme where objects may belong to more than one cluster will fit my data. I've found some literature about this subject, but i'l be glad if anyone can point me ...
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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
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 ...
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9 views

Counting repetitions

I would like to count how many times a pattern occurs in a signal and I'm confused what is the best method to do that. As I see it I could train a classifier to recognize the pattern that I'm ...
2
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1answer
30 views

Cubic Clustering Criterion using R update

I have seen other users ask about recreating SAS's CCC output in other programs. This question, Cubic clustering criterion in R, has an answer that says to use ...
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61 views

How to consider different sources for unsupervised machine learning?

In the engineering context several data sources like different kinds of measurement signals (for example distances, angles and efficiencies) are very common. If it would be possible to oberve these ...
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14 views

What is extended clustering?

What is the main difference between clustering and extended clustering? Does it simply mean that it works on larger data sets or does it mean that the clustering algorithm is extended with some other ...
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11 views

Create clusters of higher probabilty from binary data

Good evening, I've been asked to prove that there are groups of customers who have reacted to a price increase differently, specifically do some groups have a higher probability of cancelling their ...
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1answer
26 views

How do I cluster 3 columns of categorical data? [closed]

I am trying to form clusters from my data that is purely categorical: Here's an example: ...
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11 views

Type of cluster analysis for common compositions test? And parsing missing values?

I have data for different demographic groups in percentages by DMA and would like to use clustering to represent 4 or 5 common DMA structures. IF I am able to do this I would like to then show ...
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16 views

Is there a clustering method that can deal with levels/grouping

I have a matrix of pearson correlations that I would like to cluster on similarity and identify correlated networks. However the variables are part of groups and I'm not interested correlations within ...
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1answer
53 views

good practice for cluster analysis [closed]

I want to find out what are the best practice in conducting a reliable cluster analysis: Outliers: Is it necessary or not to remove the outliers in the variables to be used for cluster analysis? ...
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1answer
37 views

How to determine which variables to be used for cluster analysis

I have about 10 variables (features) and want to do cluster analysis of cases (data points). I have a number of ideas about which variables to be included for cluster analysis: Plot the variables ...
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2answers
31 views

Looking for a metric to compare clustering solutions to a reference clustering for a large dataset

I am looking for a metric to compare several clustering solutions to a reference clustering that is known to be "correct". Specifically I have a set of millions of genes, and I wish to compare ...
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25 views

Locality Sentive Hashing for Dimentionality Reduction or Feature clustering

So I have read up on LSH and Asymmetric hashing as proposed by Google for their google correlate algorithm. These work by only comparing similar items due to the multiple random hashes, however we are ...
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39 views

Clustering - Variables transformation and optimal number of clusters

I've been reading quite a bit about best approaches to clustering in cases when the true number of k is unknown. I would love to share my approach (which is a combination of best practices and ...
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2answers
45 views

What algorithm be best to use for recommendation system based on string features [closed]

I have to build a recommendation engine that will cluster users by their preferences. For example: user that looks for yellow sport GM car should get recommendations for other yellow sports cars. But ...
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10 views

Difference between biclustering and subspace clustering

I have gone through difference between bi-clustering and subspace clustering given here. But is there any difference between them in terms of mathematical definition or are they exactly same as ...
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2answers
36 views

Which metric is used in the EM algorithm for GMM training ?

My question concerns the expectation-maximisation algorithm used to estimate the hyper-parameters of a Gaussian mixture model in z multivariate setup. I understand that the EM algorithm uses the ...
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16 views

Segmentation analysis: Correct test to use?

As part of a larger study, we have collected a wealth of data on the interactions customers engage in when buying and using a service. We have tried to look at this relatively close to reality. ...
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1answer
14 views

Using centroids to find predictive cluster features

I clustered some data (rows: text documents, columns: word frequencies) using the KMeans implementation in Scikit Learn. This, like most other centroid-based clustering algorithms, returns a centroid ...
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2answers
42 views

What is maximum and its computation in the function dist() {stats} in R?

In R, we can calculate a distance matrix using the method "maximum" in the function dist() in the stats package. ...