Questions tagged [clustering]

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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What are the k-means algorithm assumptions?

I'm trying to understand what are the assumptions/hypothesis underlying the k-means clustering algorythm; specifically, I'm looking for a research/academic paper listing such hypothesis and explaining ...
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Reliable methods to validate clustering of text phrases?

Question is in the title. I have clustered the word embeddings of text phrases, and now want to try and check whether the resulting clusters are coherent enough. I have tried methods that are ...
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Why not link features instead of selecting them - Clustering

Currently, I am working on customer segmentation using their purchase data. I plan to use clustering techniques. So, my data has below info for each customer (9 features and 1 id field) Now I am ...
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Meaningful to retrieve original value after standardization using clustering

I already referred these posts here and here. Currently, I am working on customer segmentation using their purchase data. So, my data has below info for each customer Based on the above linked posts ...
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How to cluster binary data using excel or pandas dataframes? [closed]

I have a CSV with 101 rows and 150 columns. I need to find a way to segment/cluster the ID's using the Column values. It can be a machine learning approach or just using Excel techniques. At the end ...
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ANOVA interaction effect

https://i.stack.imgur.com/wdXwB.jpg If I'm performing ANOVA using the formula in R: aov(Ileibacterium ~ diet*cage, data) Context - there are 3 cages per diet with ...
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Are gaussian mixture models for clustering robust to data sparsity?

I would like to cluster customers based on their product usage data (20-40 products/dimensions) on the same scale. Overall, the data is reasonably log-normally distributed for all products (the ...
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When taking a sample of a population with clusters, how do we set a threshold so that the clusters are significant?

For example, say we have a population of about 200,000 datapoints which can be sorted into about 10 clusters. However, performing the clustering process on the entire population is not feasible. Thus ...
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How would I separate a data set into a large number of pairs?

I have $n$ observations in $\mathbb{R}^2$ (where $n$ is large and even), representing points in physical space. What I am trying to do is to produce a set of $\frac{n}{2}$ pairs, such that the ...
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Which cluster analysis method is most robust to outliers and why? [closed]

Also I would really appreciate some literature on that topic
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Using Variance of time series as input feature for time series clustering

I have a time series dataset, it is a data frame with 2000 rows and 1000 columns. Each rows is for one specific id and has a unique pattern. I want to clustering this data into multiple classes. Let ...
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RFM Customer segmentation - Why Avg monetary value instead of total monetary value?

I am trying to segment our customers based on their purchase data. And I came to know about the RFM technique (Recency, Frequency and Monetary) through these posts here, here etc. Recency - How ...
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Different Meanings of "Clusters" in Statistics

Typically, I have always come across the term "cluster" within statistics as reference to "clustering" - that is, for example the "K Means Clustering" algorithm. Recently,...
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1D cluster - Jenks optimization - Finding optimal number

I have a sample data variable shown below score 10, 11, 12, 90, 95, 97, 38, 37, 35 Instead of applying/binning data based on ...
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When we use k-means clustering with Light GBM, comparing with Random Forest

I am developping the prediction model with many parameters. As I was not satisfied by the performance of Random Forest Regression, I tried to use k-means clustering to regroup the similar variable and ...
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What methods can I used to assign cases to groups when some variables are dependant on others?

I have a sample size of 42 cases, with about 5 variables for each case. Most of these variables are measurements, and I want to assign the cases into 2 groups (condition present, and condition absent) ...
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Hierarchical clustering output 99% belongs to a cluster group

I've just done a clustering analysis using hierarchical clustering analysis in Python but the result is not what I expect. Most of them (483/485) belongs to group 1 and the rest to group 0. Is there ...
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1 answer
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Image Clustering (Unsupervised learning) on unknow class(guess less than 300)

I have 30000 unlabeled images (each image has only one character), and the content of the images is very simple, basically black lines(a language but not English) and white background. I hope to use ...
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Help! Cluster analysis of curves - grouping already grouped data

I have derived a number curves which illustrate the relationship between two (non-time) variables at different locations. I want to be able to group these curves / functions together, so that ...
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How to avoid having very different examples inside a cluster with Kmeans?

Let's say I've created some clusters with Kmeans using 5 features, the Silhouette Score for these cluster are very high, higher than 0.8, and The within-cluster sum of squares is around 130 in this ...
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Which clustering methodologies are likely to be best for this data?

I'm using the classic "use-case" example of clustering pixels in a photograph. I've tried K-means, agglomerative clustering, and DBSCAN. When I plot the RGB coordinates in 3-D space, all 3 ...
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3 votes
1 answer
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Distance between two clusters after their joining in centroid linkage

For a distance between two clusters A and B of objects given by $d_{AB}=\left \|{m_{A}-m_{B}} \right \|^{2}$ , where $m_{A}$ is the mean of the objects in cluster $A$, show that the formula ...
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High Performance Classification or Similarity Algorithim for Mixed Data Types?

I have a database holding 10-ish features that describe different breeds of dogs. They are mostly categorical features, but some provide ranges for values. Here's a demo representation of the database,...
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1 answer
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How to interpret a PCA score plot?

I get a following score plot for the breast cancer dataset. The score plot has been clustered into two categories. I want to know what inferences could I draw from it?
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Cluster Validation on SOM Codebook

I've recently been using the aweSOM R package for cluster visualisation, https://cran.r-project.org/web/packages/aweSOM/vignettes/aweSOM.html. In particular, the aweSOM package entails using ...
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Clustering while knowing the ground truth: Why would someone choose this approach?

If the ground truth of the class/cluster/segment that our observations belong to, is known in advance, why would someone choose to perform clustering instead of classification? In fact, doesn't the ...
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Treating time as a continuous variable when analyzing a stepped wedge design

I am in the process of analyzing a stepped wedge clinical trial, examining the effect of an intervention aiming to improve rates of outcomes data collection (e.g. depression scales, quality of life ...
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How can I find the start of a range in an automated way?

I am looking at real estate sales data for different regions. I bin the data by sold price range in 1000 increments (see below). I am looking to find the start of the price range or cluster in which ...
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2 votes
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What variable type to choose for a Poisson distributed variable if software package only allows for nominal, ordinal or continuous

In the area of cluster analysis I want to calculate the dissimilarity for my data (actual use case is to feed in the dissimilarities into a plotting function to calculate so called silhouette plots). ...
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Clustering high-dimensional, categorical data

I'm trying to cluster ingredients in recipes to see which recipes cluster together. This is a kaggle dataset here: https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions ...
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High dimensional behavior of Dirichlet Process-based clustering?

I have a problem stemming from Dirichlet Process Gaussian Mixture Models (DP-GMMs) in high dimension. I'll write this question so that no knowledge of DP-GMMs is needed. Let $D$ be the dimensionality ...
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Cluster a signal to minimise variance of each cluster with constraints on cluster size

Assume there is an incoming signal which may have some noise to it. This is essentially an ordered 1d data. I am looking for an algorithm which can group the data points into groups with minimum ...
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Spatial clustering with maximum group weight

I am looking for a clustering method that would allow spatial clustering of a set of points (with weights associated to each point) with maximum cohesivity, where each group of points must have at ...
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cluster by multiple linear regressions

If I have an X and Y feature, can I cluster by what linear regression two populations fall on? Consider: In the pink is one regression. In the yellow is another. I know that these two lobes are ...
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1 answer
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How to split the data into multiple (normal) distributions or clusters?

I am trying to train a machine learning model to predict yield on fields, based on multiple parameters, such as elevation, humidity, and nitrogen content. Observing the historical harvest data, I ...
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Using traj package for clustering a set of rows

I have a dataset that contains the observations of 30 people and each of them had done 20 experiments. But for simplicity let's assume I have a dataset like this: ...
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Online clustering approach

Is there any "online" clustering approach? I mean that the procedure should be like this: Can be fitted with the initial portion of data. Can be updated with the upcoming batch of data. The ...
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Multivariate Time Series Clustering with Affinity Propagation

I'm trying to implement the clustering procedure described in the paper that follows at the link https://www.hindawi.com/journals/wcmc/2021/9915315/, but I get to the algorithm 2 described and I get ...
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GMM clustering with binary and multicollinear data

I am using GMM clustering on bank data. The data have both categorical and numerical attributes. The categorical data were converted to numerical using binary encoding. I have a couple of questions: ...
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List of algorithms used to cluster weighted undirected graphs

What clustering methods are suitable for weighted graphs, where the weights cannot be interpreted as a metric ? (e.g. they do not respect the triangle inequality). At the moment I found Markov ...
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Estimating the likelihood of a Dirichlet process

I am not sure if what I'm trying to achieve makes sense or is even possible, but I'd like to do MLE on a Dirichlet process mixture model. My reasoning is the following: If we can write out the ...
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Intuitive explanation of Ward's method

I got this explanation of the Ward's method of hierarchical clustering from Malhotra et. al (2017), and I don't really get what it means: Ward’s procedure is a variance method which attempts to ...
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How to use RFM along with Predictive model?

I am looking to segment my customers based on their transactional data with us. Based on google search, I came to know about RFM matrix, which records Recency, Frequency and Monetary value-based ...
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1 vote
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Why max(b,a) in Average Silhouette?

Average Silhouette is one of the popular methods to decide number of clusters in a clustering algorithm. Why should we use max(b,a) in the denominator of the formula? So why not just avg(a,b). I guess ...
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For the given data find the clusters. Assume the relevant parameters needed [closed]

Below is the given data, how can I make clusters using symmetric matrix?
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2 votes
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Confusion on why the value of SSE is lower when a cluster looks distorted on the plot

I have a dataset of shape (29088, 11). When I apply the Kmeans where K=2 I get the following plot: Cluster C0 has 8554 points (in blue) and cluster C1 has 20534 ...
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Cluster confidence scoring

I have a scenario where I am provided a list of clusters and pairwise distance only between items in same cluster. I need to rank these clusters based on some kind of relative score from this info. e....
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1 vote
1 answer
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How to fix intersection of cluster distributions in R

I need help with a clustering task I'm doing. The essence of the problem, there is data on vegetation indices. Simple example for R ...
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In which category falls a mix of unsupervised and supvervised learning?

Here is the context of my problem: I want to classify between to classes. However, I have at disposal only non labeled data to do the training (the test set possess all labels for evaluation purposes)....
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Why does single linkage create loose clusters when it uses smallest distance between two points?

The definition of single linkage says: In single linkage method, the distance between two clusters is defined as the minimum distance between two data points in each cluster. However, different ...
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