Questions tagged [clustering]

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval etc.

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Clustering analysis for observations with lists as data

So I have several samples analyzed for their chemical composition. After data analysis, for each sample, I have a list of compounds found and their corresponding relative abundance. Some compounds are ...
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Neural network approaches for classification of time signals

I have 3D images that constitute of 2 spatial dimensions, e.g. (x,y) coordinates, with the 3rd dimension being a time signal. The signal is not periodical, but related to physical properties of medium ...
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Merging three different customer segmentation systems into one

I have been given a task where I have three existing customer segmentation systems (rule based e.g. if customer spends X in Y amount of time AND whatever then put in top spender segment is one segment,...
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What is a color blob? Is it possible to use clustering algorithm to color blob detection problem?

Wiki gives this definition of blob detection In computer vision, blob detection methods are aimed at detecting regions in a digital image that differ in properties, such as brightness or color, ...
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How to properly use approximate_predict() with HDBSCAN clusterer for text clustering (NLP)?

I have approached text clustering using HDBSCAN based on this article which describes how to do this in R. I've re-written this in Python using this library. The approach is to first calculate TF-IDF ...
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Identifying common keyphrase frequency in large dataset

I have a dataset of profiles which contain freeform text describing the work history of a number of individuals. I would like to attempt to identify frequently used words or groups of words across ...
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perform cluster on a multiple dimensional data in R

I have a data set which has 2488 samples and each sample has 13 features.Now I want to perform cluster on this data set in R but I found k-means method usually for two dimensions data.So can any one ...
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1answer
26 views

How can I adjust the legend when visualizing clusters in two dimensions?

How can I change the legend as we can see now the legend has some cluster numbers missing. How can I adjust the legend so that it can show all the cluster numbers (such as Cluster 1, Cluster 2 etc, no ...
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Is there any paper introduce an intuitive method for clustering evaluation?

I would like to use the most intuitive method like minimizing the within-cluster distance and maximizing the distance between neighbouring clusters, but not sure does this method have a name or ...
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1answer
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Image clustering with deep learning

I want to cluster image, since varibility intra and inter class of images is huge I think reducing dimensions with a convolutional autoencodeur can be a good tools. Then I apply clustering on the ...
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Clustering text documents from multiple sources

Let's say I have a set of text documents. Half of the documents are concise social media posts containing a lot of shorthand, and the other half are long news articles. Also, half of the documents ...
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1answer
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Is there any method to determine which clustering algorithm to use on a particular dataset?

I'm having a hard time getting kmeans to cluster data effectively. It fails to segment data well even for a simple attribute with 5 categories. I'm aware of DBSCAN, Hierarchical Clustering and GMM. ...
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Clustering (unsupervised learning) for uneven classes

I am looking for an unsupervised method that can see also the points that start to look different from the majority. Which clustering techniques (I use python) can be used for such data sets? I have ...
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2answers
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Applying and Visualizing k means clustering on a data set that has 9 features

I had a data set of images that I have extracted 9 numerical features that I want to apply k means clustering or hierarchical clustering to. I'm just not sure how to go about it. The tutorials I have ...
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How to compare different similarity measurements in text clustering?

I have a dataset which contains vectors generated from subtitles (each column represents a genre, each row is a movie name), my purpose is to find the most similar movie titles, I want to use ...
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DBSCAN: How does a quantile of kNN relate to the share core points?

I read this answer by Anony-Mousse to an other question related to density based clustering and how to potentially come up with an eps. It states, that if you want 90% of you points to be core points, ...
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Are there any algorithms for solid polygon clustering?

I'm looking for something like K-Means for solid polygons clustering. K-Means clusters discret points. But I want to cluster solid polygons. I don't see any problems with implementing K-Means ...
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1answer
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Find shared properties of a cluster samples

I have a dataset which contains ~15 features. With the elbow method, I found out that the optimal number of clusters is probably four. Therefore, I applied the K-means algorithm with four clusters. ...
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Dendrogram/cluster analysis of correlation matrix

I want to take a universe of potential, trade-able instruments and allocate them to portfolio managers. Traditionally, this is done using a sector classification, ...
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Not sure what to do after feature extraction for a clustering problem I'm working on

I am very new to the machine learning scene, and I'm trying to do clustering(kmeans or hierarchical clustering) on a data set of about 31k black and white images of size 200x200 (Each image is ...
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Find all recurring subgraphs/patterns of maximal size in a single undirected, labeled, connected graph

I would like to identify all subgraphs of maximal size (maximum number of nodes) that are recurrent in a single undirected, labeled, connected graph. I provide exemples of input and expected output ...
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1answer
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How to find what events cluster together?

I have a data set that looks at a five-year timespan of peoples' lives and indicates if specific events have occurred (Divorce, Birth of Child, Health Shock, etc.). ...
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Autoencoder for clustering

I would like to know if the following strategy could work. I want to cluster images, using the following 2 steps: Reduce image dimension with autoencoder apply clustering algorithm like k-means I ...
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1answer
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I have 32k black and white images. Want to do clustering on them

As the title says I'm trying to do clustering on a set of black and white images. These images are all 200x200 with black dots on a white canvas Example pics here (These are not actual photos from the ...
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Clustering with geolocation (lat/long pairs) attributes

I am trying to cluster customer behavior based on where they shop given by lat/long pairs. I also have other numeric attributes such as volume, average amount spent, etc. I am considering using ...
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1answer
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geo spatial clustering based on another feature

I have data about houses for sale, that I present over a map. Each house has coordinates ([lat,lng]) and other features. The data is only for one country, so no need to address the 180deg world wrap....
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If I have to recommend 10 movies to the users

Let's say I have some information about a user and movie data similar to the following: ...
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27 views

Gaussian Mixture Model performance with data outliers

Given Gaussian Mixture Model for clustering of data with outliers. Will the performance of it degrade with outliers or it will work as expected?
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Does the mean/median of a set sentence embedded vectors represent anything?

Please bear with me as I am new to NLP. I am specifically using tensorflow's universal sentence encoder: https://tfhub.dev/google/universal-sentence-encoder-large/3 I am clustering text based on the ...
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How to calculate classification error and index of dissimilarity(Id) and normed fit index (NFI) after LCA model in R?

I used poLCA package to get LCA models of different classes from 1-10 for 40 dichotomous variables. Now, I want to calculate ...
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Different approaches for categorical non-ordered data clustering in R

I'm trying to find different clustering approaches for only categorical data in R, so far I found: klaR for kmode cba for rock Hierarchical clustering (agglomerative or divisive) with a categorical ...
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1answer
20 views

ML Approach for Getting List of Observations with Similar Features (Discrete+Continuous)

I have a dataset with 19k observations. Each has approximately 448 features: - Text description turned into vectors of size 300 - 16 categorical variables represented numerically - The remainder ...
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Clustering of words based on ability to predict variation in other variables

I have a data set of about 1000 observations like so: ...
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21 views

Cluster based on both positions and similarity scores

I have a dataframe position giving me the x and y positions of 87 points. I also have a 87 x 87 similarity matrix giving me the pairwise similarity scores between ...
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Regarding sklearn adjusted_rand_score in k means clustering

A dataset is given consisting of target class as categorical.K means was applied to it for clustering the data and got the corresponding cluster labels. But how to put the values in adjusted Rand ...
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1answer
35 views

Is there an algorithm for categorizing unlabeled samples into K classes? [closed]

I am not sure if this would be considered unsupervised, or semi-supervised learning. I am looking for an algorithm that will take N input arrays of features, and then cluster samples(not features) ...
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2answers
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Which clustering method is recommended to start with when all the variables are categorical

Which clustering method (k-means, Hierarchical, PCA etc) is recommended to start with when all the predictor variables (16 of them) are categorical, consisting of 3 to 7 levels. I’m assuming k-means ...
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1answer
39 views

How to clusterize points lying on the sphere

I want to cluster protein conformations by dihedrals angles. My point is an n-dimensional vector, where is n - number of dihedral angles. I think I can't use Euclidean distance for distance metric ...
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1answer
74 views

DBSCAN clustering on document [updated]?

I am new in topic modeling and text clustering domain and I am trying to learn more. I would like to use the DBSCAN to cluster the text data. There are many posts and sources on how to implement the ...
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Design / Choice of Autoencoder to classify temporal pattern in images

Suppose I have a temporal stack of images of shape $m \times n \times k$ where shape of each image is $m \times n$ and $k$ represents the temporal dimension. In this context, I am trying to detect and ...
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1answer
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How to cluster/identify points away from a regression line

For many vine plots, I have NDVI and Leaf Area values for each vine. I already know that NDVI and LA has a strong positive correlation as you can see in this picture. But as you can see too, there ...
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2answers
39 views

Why does changing the cluster number change the plot in Kmeans?

This might be a dumb questions but I can't find the answer to it. I don't have the perfect mathematical understanding of kmeans, so apologies if it is. I'm just wondering why I see a different plot ...
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Transformation of matrix with missing values for hierarchical clustering

Comparing different variables, I got a matrix with lots of missing values. How do I have to transform the matrix below for hierarchical clustering? What I have already tried: ...
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2answers
49 views

Url string processing: what is the best way?

I have ~1000 different news websites and I scraped and saved all the internal url links for each website. For instance, the website dcgazette.com has a 2MB text file with associated urls: 1) https://...
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preprocessing : Predicting with Multiple+Multivariate+Multitrend time series data

I am trying to predict the value of a variable in a multivariate time series; of which I have multiple time datasets (one system = one dataset containing 10 variables in time and average 120,000 rows) ...
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1answer
29 views

Feature selection or Dimension reduction in unsupervised learning

I'm trying to do Embedded clustering using kmeans. This is customer data, so it involves a lot of sentences, so I'm using the universal sentence encoder before clustering. But I should be doing a ...
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1answer
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Clustering not working as expected

I have clusters as shown in the picture below. The data is 2d : the two parameters are error and time. I tried using the following clustering algorithms: 1) kmeans:clusters are spherical. This algo ...
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1answer
38 views

Stationary time series for clustering algorithms

I have a set of time series data that I would like to feed into a clustering algorithm (like k-means, using dynamic time warping as the distance function). After standardizing the data with mean 0 and ...
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2answers
80 views

Anomaly detection k-means in Time Series

I'm trying to use k-means to detect anomalies in the Amount column. I have the following part of my dataset: ...
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1answer
31 views

Clustering with custom criterion (minimum cluster weight)

Edit: following comment from @anony-mousse, I'm changing the question to search for a general clustering approach that matches this criterion (minimum weight per cluster). I am to use a clustering ...