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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19 views

Determining the effect of combinations of independent variables (customer charateristics) on dependent variables (customer value)

I have lots of transactional and demographic (etc.) data about my customers and I want to understand: "What are the characteristics (age, profession etc.) of valuable customers?" To do this ...
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I want to apply Time Series Clustering to time series data consisting on: Level & Growth, but I have only found algorithms for one series

Is there any implementation of Time Series Clustering which allows me to segment using two or more series of the same phenomenon both as input for the algorithm? Suppose I have $A_{i,t}=X_{i,t}$ and $...
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Fuzzy c-means clustering line graph data

How do you cluster data that are on line graph form using fuzzy c-means? Do you cluster each data per value of horizontal axis? I have the example below. The first photo is the data gathered every 30 ...
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Cluster analysis for categorical data with multiple values per category

I did a cluster analysis for my dataset which is categorical with multiple values coded for each category. Basically, I encoded my data with one-hot encoding method and run a k-mode analysis. However, ...
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When use standardization, normalization or both?

I have a dataset with variables with different scales as shown in the figure below. I need to group individuals together and I'm testing algorithms like Kmeans and DBScan. In all tests I'm extracting ...
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Find most important and least important features for clustering algorithm

I am experimenting with clustering algorithms, like K-Means. Right now, I use all variables as input for the clustering algorithm. I am wondering if it is appropriate to do feature selection for ...
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Clustering with hierarchical data dependencies

I am currently looking into how to cluster data with hierarchical dependencies. An example of a problem that I want to cluster: we would like to cluster cities to identify similar characteristics with ...
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Clustering method that allows to choose clusters' size

I have a multilabel dataset showing an extreme case of imbalance. I was thinking of clustering the less populated classes into bigger clusters of size at least N. My question: is there a clustering ...
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Topic modelling on long documents: intra document clustering first

I have a collection (around 1000) of very noisy, similar documents, that are each very long (>10 pages - 600 paragraphs) with multiple subsections - I want to perform topic modelling across the ...
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Silhouette Score for different Clustering algorithms

I am trying to compare different clustering algorithms on a dataset and compare the model performance. Since the dataset is quite big (56 features), I applied PCA to reduce the number of features to ...
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Silhouette score for optimal k value (k prototype in python)

I am trying to cluster using k prototypes algorithm as my data has both categorical and continuous variables I found this answer explaining the elbow method with k prototype https://stackoverflow.com/...
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How to compare two clusterings?

I am performing clustering on some general problem. I often end up with similar clusters, up to a permutation. That is, I will get something like this : A header methode 1 - cluster 1 methode 1 - ...
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How can I calculate the level of agreement between my K-Means cluster labels and my ground truth labels in R?

I have made a K-Means clustering from 3 rasters with various values of k (k=2, k=4, k=7) and would like to know which values of k explains the most variance in my ground-truth data or the value of k ...
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Supervised clustering use case?

I'm currently working in a problem, where I think a supervised clustering approach might be a good candidate, but I'm not sure and haven't really worked with such scenario before. Let me break it down:...
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Clustering 3D image voxels based on their location and value

My goal is to detect whether a MRI image contains an anomaly and the location of the anomaly. In my dataset I have MRI brain images which contain values of electrical conductivity of brain tissues. ...
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How can improve the performance of clustering algorithm concerning similar/same records?

I want to check/experiment efficiency improvement of clustering algorithm under the title of Statistical preprocessing was done by including statistical frequency (counts) into dataframe concerning ...
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Hierarchical and fuzzy C-means clustering

I computed hierarchical clustering and the best classification requires k=8 clusters. I wanted to find the probability of belonging to a cluster for each unit so I used fuzzy C-means clustering with k=...
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Clustering Strings Based on Similar Word Sequences

In my dataset I have a feature having below data : Input Feature Brain Dementia Routine(Comfortone) Morning Check Dementia Brain-Routine(Comfortone) Brain MRA Routine (Comfortone) Brain-Dementia/...
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Aggregating Silhouette values to Silhouette coefficient

Since there is no Silhouette calculation for mixed data types in Python k-prototypes packages that I know of (e.g. this one), I wrote my own code to obtain the Silhouette values. I need to aggregate ...
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what is the best approach for clustering and prediction with dataset having both categorical and numerical columns in python

I have a dataset with survey data asking if people want to buy/have bought certain products. Columns are like: product name survey type/location targeted group, e.g gender, age # of positive answers, ...
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How to evaluate unsupervised Anomaly Detection using k-means

I'm trying out different anomaly detection models and would love to hear opinion on my idea from somebody experienced. My goal is to perform anomaly detection with different models and to give each ...
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Crossover Operation for 1-dimensional problems in Differential Evolution

I am using Differential Evolution (DE/best/1/bin) for optimizing a 1-dimensional function i.e. My Population has floating point values (Population size=10, hence 10 floating point numbers) and I have ...
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Find business vertical of a website just by its URL or cluster similar website by its url

I have been exploring this problem a lot about just using the website url to tag or cluster them as per their business domain. For example: ...
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K-means++ with cosine distance

I am wondering how to implement k-means++ with cosine distance, acording to quote below (wikipedia), which says, that distance needs to be squared. But with square is lost direction of distance which ...
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Clustering: How to find which point in a cluster in the closest to the cluster centroid while using kprototype

I have a dataset which contains both numeric and categorical data. In order to carry out clustering in python I have applied kprototype which is the mixed form of kmeans to be used in such cases. I ...
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Creating a popularity index from multivariate data

I have some data from an ecommerce website with features like product_name, product_category product_link, product_id, free_delivery(1 or 0), price, discount, avg_rating, number of reviews, ...
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What is the principial difference between zero-shot learning and k-NN and clusterization based methods?

One can consider clustering and k-NN to be a zero-shot, too? I think there is no much principal difference, except using some neural network architecture (usually it is a transformer) which is used to ...
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The best ML algorithm to give recommendations to fill in a selection

I have a ML problem where I want to suggest a combination of options to the user based on their current choice of options. The options are all boolean (selected or not selected), there are several ...
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Definition of local minimum in k-means algorithm

I know what a local minimum for a function $f:\mathbb{R}^n→\mathbb{R}$ is. The error function in a k-means algorithm gets a vector of assignments and a vector of centers. How does the term local ...
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How to work out the optimum threshold for BIRCH clustering

I am working with a large dataset so I thought that BIRCH would be an ideal clustering algorithm to apply. Can anyone suggest how I can work out what the optimum threshold would be?
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Geospatial clustering plot with zoom in Python?

I need to construct an interactive clustering plot. Ideally as the user zooms in the clusters would split-up into smaller clusters at certain zoom levels. I am planning to have several discrete levels ...
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How to visualize a hierarchical clustering as a tree of labelled nodes in Python?

The chapter "Normalized Information Distance", visualizes a hierarchical clustering as a tree of nodes with labels: Unfortunately I cannot find out how to replicate this visualization, ...
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Choice of the number of topics (clusters) in textual data

I have a social science background and I'm doing a text mining project. I'm looking for advice about the choice of the number of topics/clusters when analyzing textual data. In particular, I'm ...
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Method of choosing features for better clustering?

I'm working on a project where I need to cluster data. After doing all the usual steps (in no distinct order: one-hot/BaseN encoding categorical data, doing a Quantile Transform due to none of the ...
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How to run hdbscan clustering faster?

I'm using hdbscan to cluster embedding output from BERT, which took in a data file of >150k chat messages. The embedding process took a little over 4 minutes, but as of this writing the hdbscan ...
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How to calculate diameter of clusters for DBSCAN?

I've created several clusters for my task. Now I'd like to know the distance among the far points in each cluster. ...
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How to cluster skills in job domain?

I have a problem related to clustering, where i need to cluster skill set from job domain. Let's say, in a resume a candidate can mention they familiarity with amazon s3 bucket. But each people can ...
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How to load any particular folder files from a zip dataset

Twitter is a great source of information. Using The Health-News-Tweets.zip dataset contains tweets by different agencies like BBC Health, CBC Health, etc. I will ...
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Find correlation within vectorised data

I am at the feature selection phase of my project but I have my vectorised data. Is there a way to find highly correlated features and then remove them? After this I would then like to remove features ...
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Clustering Mixed Data Types

I have a dateset with 67 variables consists of 9 numeric and 58 factor variables. which clustering method do you think I can use?
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Transforming time series into static features?

I'm working on a side project where I have a mixture of static data and time series, and the goal would be to perform clustering on the data. There's a bunch of data sources, but basically the main ...
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Is it possible to cluster unseen data using transductive algorithms like DBSCAN, OPTICS, Spectral Clustering, Agglomerative clustering

I am trying to solve a clustering problem. In general for K-Means clustering we fit the data and whenever we have a new data/sample we use ...
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I am looking for general image-based clustering methods

My task is to cluster some images, I decided to use the VGG model to extract the features and then use K-Means to cluster these features. But my question: When I use a VGG as a feature extractor, I ...
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Statistical method to validate predicted outliers

I was trying to make a clustering-based unsupervised anomaly detection on a large high-dimensional dataset. Roughly saying the points not lied inside all the clusters are defined as anomalies or ...
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Use clusters as dependent variables

I wanted to ask anyone was aware of a type of two-stage analysis where clusters are used as a dependent variable in prediction models? For example, suppose I had used an unsupervised model based on ...
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clusteing along the road network

I have coordinates of accidents and coordinates of road network. I wish to cluster accidents along the road network. Is there any clustering technique available. (e.g. Arial distance between two ...
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1answer
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2 Most probable labels with Gaussian Mixture Model Clustering

I want to get the two most probable labels for each sample in my X. A little context: I am working on a clustering project where I have 1.6M samples that have to be clustered into 12 clusters. First, ...
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Clustering dataset with and without estimating means (no EM algorithm)

Given a dataset $D$ of the form $$ D = \{ (x_0,y_0), (x_1,y_1),\ldots,(x_{n},y_n) $$ sampled from a Gaussian mixture model with identity covariance matrices, I want to understand what are my options ...
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Document Clustering for given specific clusters in python

How can we classify text in to given specific number of clusters in python? I'm aware that the number of clusters can be specified using some mechanisms like k-means but I need to classify the given ...
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Affinity propagation did not converge, this model will not have any cluster centers

When I try to cluster using affinity propagation, the below error occurs and the number of clusters is one. ...

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