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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regression by grouping the dependent variable

I have a large dataset exploring the effects of the independent variables on the dependent variable using Poisson regression since the dependent variable is a count variable. However, the range of the ...
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Data clustering with multiple observations for one subject in R

I have the following data frame: ...
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Store Segmentation/Clustering based on SKU performance, Demographics/ Climate in Planogram/Assortment?

I have a Dataset of more than 500 stores which I want to cluster based on their SKUs performance this because of the space restrictions the stores have. The idea is to create as much clusters ...
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What metrics can be used to evaluate each cluster in clustering

I am clustering a dataset, where the binary ground truth (positive/negative samples) is known. I am looking for specific clusters that show high homogeneity/purity. I know that there are many metrics ...
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How can I cluster plant biomass and grain weight for different plant varieties using Ward's method based clustering?

I have plant biomass and grain weight data for different plant varieties which I now need to cluster. Do I need to define the number of clusters if using Ward's method and Squared Euclidean distance ...
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Similarity Metric Validation

I want to score a number of similarity metrics, i.e. given a function s(x,y) which returns a number that is higher the more similar x and y are. I'm want to objectively score a number of different ...
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Why does k-means have more bias than spectral clustering and GMM?

I ran into a 2019-Entrance Exam question as follows: Which of the following algorithm has the higher bias? GMM GMM (identity covariance matrix) spectral clustering k-means The answer mentioned is (...
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Should one-dimensional data be normalized for K-Means clustering?

Data normalization is important prior to K-Means clustering when there are multiple variables in the clustered data set. Data centering and scaling (for instance using Z-score) can change the relative ...
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Gaussian Mixture Models and distance matrix

I have a (euclidean) distance matrix and I want to perform GMM clustering. I read in another post (gaussian mixture model - approximate a matrix) that I could apply MDS or PCA to this matrix and use ...
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Clustering after t-SNE in R

As explained here, t-SNE maps high dimensional data such as word embedding into a lower dimension in such that the distance between two words roughly describe the similarity. It also begins to create ...
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Instrumental Variable - clustering and standard errors, in both stages?

I was wondering whether in an instrumental variable procedure, you do the clustering and standard errors in both stages or just the final stage. Wooldridge (fifth edition, section 15.6) in the first ...
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Cluster a set of strings based on similarity while optimizing redundancy

The goal is to cluster a set of strings based on similarity while optimizing redundancy. Example: ...
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Correlation structure of High Dimensional data in R

I have p>>n case, with 10K explanatory variables that I would like to partition/cluster into modules so that the correlation within groups is maximized and correlation between groups is ...
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Pre-/Post Design controlling for pre-differences, clustering and survey weights with lmer

I'm only getting started to use R for a research project about multimorbidity in old age. Multimorbidity was measured twice at an interval of 5 years (t1, t2) in 840 participants. There was no ...
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R Library for Ordinal Regression with Split-Plot (Cluster?, Repeated Measures?) Structure

I have a data set with the following structure. The response is ordinal. There is an experimental factor (with two treatment levels, each treatment level applied to a different sample of subjects). ...
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Characteristic-Based Clustering of Time Series

I'm trying to teach myself how to apply clustering algorithms to time-series data. I've recently come across a paper (https://robjhyndman.com/papers/wang.pdf) about using time series characteristics ...
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Use pairwise correlation matrix for clustering

Suppose I have random variables $X_1,\dots,X_d$. Suppose I collected some data, say $n$, and want to calculate the sample correlation matrix of the $d$ random variables. However due to missing data, ...
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Find all possible clusterizations

I need help to find all possible clusterizations via the k-means method in Python. Let's assume for simplicity that I have the following table: height | weight | country of origin (X/Y/Z) | flag (1/0) ...
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Marginal Distribution of Last Variable in Chinese Restaurant Process?

I want to know whether a closed-form expression exists for the marginal distribution of the last variable drawn from a Chinese Restaurant Process. To be more specific, suppose I have $z_1, z_2, ..., ...
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How to avoid k-means to merge two groups of points into one cluster?

I've implemented a k-means clustering algorithm, but in some cases (~12%) a situation like that happened: In these cases, my algorithm is creating one cluster for both the yellow and blue group of ...
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Radius calculation for cluster

I am working on a clustering algorithm using cosine distance for measuring similarity between points. Can anyone help with a formula to find the radius of the cluster based on aggregate statistics of ...
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Unsupervised training a Neural network to identify/estimate optimal threshold (scalar) between 2 clusters/distributions

I want to train a neural network to identify "optimal" threshold value which Separates between 2 clusters/distributions given a data set or a histogram. For examle, say I have a 1-...
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Acceptable SSE (sum of squared errors) for K-means

I am developing a k-means clustering algorithm, and I have obtained the ideal number of clusters based on the elbow method. However, despite the fact that the error diminishes a lot with the number of ...
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Network clustering stability using bootstrapping techniques

I use the standard modularity-maximisation Louvain clustering method to partition a large undirected network into communities. I fear the result of the partition is quite fragile. Is there a standard ...
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Evaluating the performance of tracking multiple objects detected with object detection

I have a ground truth dataset where the objects have been manually annotated and each object have been provided an ID that is consistent through time. There are no false positives or false negatives ...
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Performing Discriminant Analysis after K-means clustering on the same data

Let's say I perform a k-means clustering analysis on my data and I find three distinct groups. Afterwards I perform a Discriminant analysis on the same data with the clustering from k-means as the ...
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obtaining proximity matrix from random forest for unsupervised scenario in R

I recently came across the concept of proximity matrix in random forests (see for example this great StatQuest video). This can easily be obtained in the regression or classification scenario like so: ...
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Grouping by similarity

I would like to find a way/algorithm to group people into, say, four groups by their answer similarity to yes/no questions. So, each pair of people in one group would have given the same answers for a ...
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How to do clustering of fft values of a time series dataset?

I have a time series dataset, I have computed its fft. But I want to know if there is any specific clustering technique for fft values or can I use clustering techniques such as kmeans,heirachial?
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How should I approach clustering with features with different lengths for different data?

I'm trying to cluster different audio files and will be using features that vary in length with the length of the audio file. For example, one of the features is the pitch over time of each audio ...
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How should I do clustering when I have a mix of single-element and time series features?

I'm trying to cluster together different short audio files based on the zero-crossing rate (an integer) and the energy, spectral centroid, and spectral bandwidth (time-variant values). I've decided ...
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The silhouette coefficient does not increase monotonically

Recently, I have done some clustering with K-means and I use the Silhouette coefficient to estimate the right number of clusters (K). However, my Silhouette coefficient does not increase monotonously ...
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How to interpret clustering performance measures of the R caret package ( iris dataset )?

Good afternoon ! Using the caret package in R, I computed the following performance measures of a clustering algorithm : ...
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Clustering algorithm with special properties

Let $y$ be a real-valued vector in $\mathbb{R}^p$. Given a dataset $D=\{y_1,\dots,y_m\}$, I want to find every possible partitions $\mathcal{P}=\{\mathsf{w}_1,\dots,\mathsf{w}_m\}$ of $D$ that ...
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Comparison of clustering methods for mixed data

Sorry if the question is not suitable for this site. If so, I will remove it. Simply, I am looking for reviews and comparisons between clustering methods which could be used for mixed data "...
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social network measure of node's crowdedness

The undirected network below shows the interaction of workers (blue nodes) with products worked on during a time period. The thickness of the edges/arcs indicates how much time the worker spends on a ...
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within cluster sum of squared distances [duplicate]

I am using k-means clustering algorithm to cluster a given dataset into two. I took two different initial points and run k-means clustering algorithm for each initial points. With the first initial ...
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within cluster sum of squared distances

I am using k-means clustering algorithm to cluster a given dataset into two. I took two different initial points and run k-means clustering algorithm for each initial points. With the first initial ...
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Defining a useful sense of similarity between binary variables

I would like to visualize a set of dependent binary variables $x_1,...,x_n$ in 2d (where conceptually, 1/0 means presence/absence). ($E x_i$ is very low, $n$ is about 100, and there are hundreds of ...
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1answer
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KNN and K-means, very different but possible equivalency?

Why does the k-nearest neighbor algorithm and k-means clustering algorithm with $k=1$ act the same?
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1answer
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Vatiational inference in GMM

I am learning about VI and am implementing a GMM model for clustering using variational inference. However, my implementation is not fitting the data at all, even when initializing the cluster means ...
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Find the ideal cluster

So, I and some other colleagues developed a hierarchical clustering algorithm to basically find the main clusters involving agricultural industries according to a particular city (e.g. London city).. ...
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Gaussian mixture model for image labelling task

I'm trying to solve an image labelling task by using Gaussian Mixture Models. The total number of classes in my dataset is 9, each representing a different variety of vegetable (Class1, Class2, Class3)...
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Use clusters.stats function from a hierarchical clustering in R

I would like a great help from you. I used the cluster.stats function that is part of the fpc package to compare the similarity of two custer solutions using a ...
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Confusion about ICC of nulll model and full model?

I am trying to apply multi-level logistic regression (I have 15 schools as a cluster) In my null model, ICC is 0.1047884. When I ran my full model, the ICC remain 0.1087846. However, LR test vs. ...
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Machine Learning for choosing the right cover box to contain smaller item boxes

I am studying on choosing the right box to contain item boxes. When customers order items, items have their own cases and ordered items are packed with the right cover box to contain them to deliver. ...
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Modified distance functions for a cluster analysis

I'm developing some software to allow users to perform various kinds of clustering on some data using a pairwise distance matrix (k-medoids is the main method). I would like to allow the user to tune ...
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How to group countries with similar age distributions?

How can I group the world's 200-odd countries into (say) ten groups, with each group's countries having 'similar' age distributions? I want to compare COVID-19 fatality rates across countries. But the ...
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Using additional features while indexing in Approximate Nearest Neighbors

I am trying to develop a recommender system that suggests top 10 workers suitable for a task at hand. For the features, I have work type, criticality and location of work in both my historical and ...
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Need Concrete Example of Where a Frequentist Clustering Technique Outperforms the Existing Bayesian Clustering Techniques

I'm looking for a concrete example of frequentist clustering outperforming Bayesian clustering (using the best Bayesian algorithm for the problem, using the testing criteria below). There are many ...

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