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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Cluster Analysis for Website Data [duplicate]

I want to perform cluster analysis on the data of a website. The data is mainly visitor history(97000 rows) and has following variables: a)User Device Category b) Traffic Marketing Channel c) Traffic ...
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How to cluster an 1-D array by K-means or any other algorithm using scikit-learn? [closed]

I have an one dimensional toy array X. I want to cluster the data into some numbers of clusters.But when I try to fit my data in scikit-learn K-Means function it shows ValueError: n_samples=1 ...
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Clustering algorithm for multisorted data

I have two sets (“sorts”) $A$ and $B$, and a multiset $R$ whose underlying set is a subset of $A \times B$. I need a clustering algorithm, where each generated cluster consists of two subsets $S \...
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How should I determine what seed to use while using “flexclust” package?

I am trying to find a clustering solution with the help of flexclust package in R. The following code has been adapted from the vignette for the ...
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How to cluster the data? [duplicate]

I have a data that looks like below and I want to cluster them. What would be the best algorithm to apply for clustering such data. Thanks
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Best Clustering Technique for Probability Scores

I have a data which which have 17 variables i.e. 17-Dimension data. The Data is a result of Max-Diff exercise which is performed for ranking these 17 attributes and have comparative preference/...
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Clustering of distributions in R

I have a set of distributions corresponding to predictions for how each of hundreds of players will perform. I am looking to identify the distinct distributions of players. In other words, I'm ...
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Clustering for mixed variable type [duplicate]

I have the following data set with mixed variable types ...
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Using machine learning to detect errors

I have a scenario I believe could benefit from some of the statistical models used in ML but I need a little guidance. Or someone to tell me I'm way off base with my idea. The scenario: I have ...
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Zero values in Rtsne

I want to use the R implementation of t-distributed stochastic neighbor embedding Rtsne to sort out a large number of sets of numbers of cDNA reads from different ...
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2answers
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Which one has higher square sum error using K-means?

I have trouble in coming out with a straightforward way to know which one is better in K-means when clustering considering SSE(squared sum error). Thanks.
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Clustering binary data [duplicate]

How can I cluster household data which has binary variables like owns_car, rented_house (which all had answers in yes/no and being converted to 0/1). My data has 86 dimensions and about 3 lakh rows. ...
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Is it task for machine learning?

I have about 100,000 products in my database. I have files with products in different formats which are some variation of "Apple iPhone 6s 64GB Gray $100". I need to find the same product in another ...
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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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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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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
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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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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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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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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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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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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 sentence....
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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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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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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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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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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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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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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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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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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 expected ...
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Cluster analysis vs Factor analysis as a means for “grouping” variables or cases: the differences

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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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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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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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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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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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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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 ...
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1answer
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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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1answer
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How to consider different samples in functional data clustering?

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 observe these ...
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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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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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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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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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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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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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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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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 ...