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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43 views

How to calculate Fuzzy C-Means problem by hand

I figured that this doubt next can interest another students like me and help others also that are trying to understand mathematically the fuzzy c-means mathematical mechanism already that some books ...
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The actual results and results from pickle files are not matching in pandas for DBSCAN clustering

I've built a DBSCAN clustering model. The output result and the result after using the pickle files are not matching. Based on HD and MC column, I am clustering WT column. ...
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Clustering data by multiple values

I am trying to find a way to cluster/group students by their knowledge of different subjects. Given following as an example: ...
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How would one get the HTML structure of a web page as a numeric vector? [closed]

Suppose you want to cluster, or classify, web pages inside a domain. In the same domain similar web pages always have the same structure more or less. (think of every product page an e-commerce would ...
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40 views

What is an appropriate machine learning technique to analyse development of status over time? [closed]

I have a dataset as follows (not the actual data, but representative): ...
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34 views

How to structure my data into features and targets for PCA on Big Data?

I want to apply the PCA algorithm from Scikit-Learn.(https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html ) At the part where I have to separate the features and the ...
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18 views

Are DBSCAN and dbscan from the sklearn.cluster package different?

I'm new to DBSCAN. I was looking at a few examples online and came across a few instances where the following lines were used while importing the dbscan module: <...
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1answer
22 views

Suggestion on tag clustering visualization

I have a database of tags given by users to the product. For example user; product; tag 1; A; Tag1 1; A; Tag2 2; A; Tag1 2; B; Tag1 .. .. I am trying to cluster ...
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32 views

Unsupervised Clustering high dimentional data not having estimation for K

I have a dataset (all numerical) of 50K records containing 500 features. we are trying to find fingerprints. Meaning that we would like to cluster the data and report one of the nodes in each cluster ...
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32 views

trade offs between number of features with its score

I am running k-mean clustering on ~200000 samples. The dataset has in total 14 features. One feature is id and the rest are categorical. I have been playing with ...
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25 views

External cluster evaluation for a varying number of cluster

There are many external clustering indices like (Adjusted) mutual information, (Adjusted) Rand index, and many more. However, they are not very good at comparing clusterings where the number of ...
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40 views

Modifying BERT sentence encodings

I'm using BERT to encode sentences. The sentences I'm encoding are quite similar, meaning they all belong to the same overall topic. Therefor, I am using another parameter for measuring similarity. ...
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Clustering of Sparse Time Series

I have a dataset of certain user activity per week (e.g. purchasing an item or using a service per week) for the past 52 weeks and for 100K+ users. The matrix is very sparse (85% of the entries are ...
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161 views

Time series clustering using dynamic time warping and agglomerative clustering

I'm new to data science and I'm currently working on a project to classify electricity consumption profiles. This consists of electricity meter readings taken from sites on a half-hourly interval ...
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28 views

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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12 views

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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30 views

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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138 views

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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2answers
28 views

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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31 views

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
95 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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18 views

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
25 views

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
41 views

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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37 views

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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2answers
121 views

Are there any algorithms for solid polygon clustering?

I'm looking for something like K-Means for dividing solid polygons into regions. K-Means clusters discrete points. But I want to cluster (that is, partition) the points of solid polygons. I don't see ...
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1answer
20 views

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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18 views

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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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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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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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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1answer
194 views

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
28 views

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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3answers
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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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36 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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69 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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78 views

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
36 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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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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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 ...