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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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15 views

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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Clustering calculation [closed]

Search, and research I am following the classic Morgan Kauffman book on data mining. However, I am running into an issue with clustering. The explanations that I am reviewing on the internet are ...
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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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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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Agglomerative Clustering (average linkage) and Pearson Correlation

Does having a positive or negative correlation between features being clustered affect the agglomerative clustering result? I have three columns in my dataset, and I'm trying to figure out if I should ...
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Visualise KMeans clusters in 2d, when number of input features is greater than 2

I am using KMeans clustering in Python (Scikit-learn) with around 70 input features per sample and a little over 1,000 samples. It is performing rather well, which is good. However, I would quite like ...
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Dataset that's already encoded, mixed types, methods for EDA and clustering?

I have a dataset that is already encoded (so it looks like table 2) with numbers (ie. each state & flavor have an assigned number, likeliness to try new flavor is on a scale 1-5, etc.) I would ...
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k-means clustering over columns not rows

I have a table with 100K+ rows and 100+ columns all numeric. Rather than using k-means to cluster rows together (and creating a new column category that labels each ...
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Clustering of events

I've a sequence of time ordered set of points: for each $t=1...T$ I have a set of points $(x_{t,i},y_{t,i})$. I need to cluster them together in space-time. I don't know however a priori the number of ...
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Kmeans with Word2Vec model unexpected results

I'm trying to play around with unsupervised NLP using Word2Vec. So far, the data i used is very small, but that is because I am just testing to see how Kmeans will work. The Kmeans was performed first ...
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Performance measurement of an event extraction system

I have developed an event extraction system from text documents. It first clusters the data corpus and extracts answers for what, when and where questions. Final answers are determined by using a ...
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Implementing mean shift clustering in spatio-temporal domain?

We used meanshift clustering in the spatio-temporal domain (i.e., [x, y, t] with a kernel of size [32, 32, 200]). We treat clusters with at least 2 samples as fixations and use cluster center as the ...
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How do you describe the clusters created by k-means?

I understand how the clustering algorithm k-means works and I can map any new point to any of the lusters using the predict function. What I want to understand is: how can I describe the clusters? For ...
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Is it always possible to get well-defined clusters from the data?

I have TV watching data and I have been trying to cluster it to get different sets of watchers. My dataset consists of 64 features (such as total watching time, percent of ads skipped, movies vs. ...
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How to model/classify user's activity based on BEHACOM dataset

I want to classify user's activity from BEHACOM dataset into three types: active, middle and inactive considering keystroke count, move movement average duration and click speed average duration over ...
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What is the name of this supervised clustering algorithm?

I am doing deep learning research in supervised contrastive learning. The problem I am interested in can be simplified into the below scenario. And I am wondering what is the name of the following ...
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Results interpretation of AgglomerativeClustering labelling

First of all I would like to say that I'm quite new to python and even more new to scikit, and I'm also a self learner, so please forgive my banal question, but it doesn't look banal to me. So, I have ...
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Is K-Means ++ is the best method to initialize centroids?

I recently came across different initialization strategies like random, parition and kmeans ++. In many places it is mentioned K-means++ has better initiailization strategy. Is it true? or Is there ...
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clustering data set based on the similarity of tree structure

I have a data set (>5000). each individual record of data is structured as a multilevel n-ary tree (>200 nodes). The tree node identifiers are unique within the tree. but the same identifiers ...
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best method to pick correct number of clusters?

Apart from ELBOW rule and silhoutte coefficient is there any other better methods to pick correct number of clusters in recent years ?
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Is subspace clustering better than spectral clustering?

After reading a few papers about subspace clustering (e.g. the one by Elhamifar and Vidal), it looks like subspace clustering includes scenario of applying spectral clustering: it works for data ...
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Visualization suggestion:-

I have a data frame like below:- Here I have 194 countries and the columns are fan_out values which is in percent of the total population. Like for country AD, the total fan_out value is 2.24 -06 % of ...
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Tips for clustering rows of a gigantic "distance" matrix

I've been assigned to the following task: I was given 1,000,000 data points and was asked to create a sort of distance matrix and to cluster the rows. So this matrix is 1,000,000 x 1,000,000 which is ...
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The proper way of resampling data to be used for clustering when the outliers are the important data points

I have a dataset that is bigger than I need it to be. In fact, bigger than my hardware can handle. So I'm trying to lower the number of samples. And I'm not sure what is the right approach to do so. ...

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