Questions tagged [hierarchical-clustering]

Hierarchical cluster analysis is a method of cluster analysis which builds, by steps, a hierarchy of clusters, a dendrogram. Most popular is agglomerative hierarchical clustering (HAC) which starts from individual objects and collects them into bigger and bigger clusters.

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Conditional intraclass correlation (ICC) from a linear mixed model as evidence for test-retest reliability?

In my experiment with two conditions (between-subjects design), participants completed a single-item scale three times: (1) before the experimental manipulation, (2) after the experimental ...
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Comparing two cluster solutions

I'm trying to complete my dissertation which has become somewhat challenging as all my professors have gone AWOL. Any suggestions would be appreciated! My biggest question now is: how can I compare ...
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Significance test for variables within a cluster

I have performed a cluster analysis in R: ...
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Understanding the difference between standardization and normalizing for hierarchical clustering

I am trying to understand the difference between normalization and standardization for the hierarchical clustering. I read the documentation and some posts like this and this as well as several SO ...
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Prerequisites/Checks for performing clustering

What are the checks that should be done on our data before performing clustering? Like how to check whether the dataset contains clusters of equal size/density or the clusters present in the dataset ...
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Gain scores as an outcome in HLM?

I'm analyzing data from a single-group, pre-post design, trying to see if student beliefs change after an intervention. Students are nested in classrooms, and there is a significant amount of ...
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Hierarchical graph clustering using a kernel matrix in R

I have a set of 9 directed graphs of differing sizes and I'd like to use graph clustering to create a dendrogram illustrating their structural similarity, similar to what's done in the NetConfer ...
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how to set Birch parameter values?

Birch is an incremental clustering algorithm based on a hierarchical data structure, CF-Tree. It produces results close to those of k-means, i.e. spherical clusters. However, although it does not ...
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Determining differences between hierarchical clustering groups with ANOVA: Is my method correct?

I have 20 different hybrids of plants (three replications each in a randomized complete block design), and I have taken measurements on subsamples within each of the plot. In order to see if there are ...
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stepwise clustering

Assume that we have 3 features in our dataset and we aim to cluster them. Assume that first two variables are in the same scale and have a "similar nature" and the third one has totally different ...
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1answer
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Survey design for multilevel models

I am trying to fit a multilevel model with complex survey design data in Stata. However, my model levels do not correspond to my survey design stages. My survey design is based on sal primary ...
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How do I specify a 3 level multilevel model in r?

I want to first examine a growth model in my multilevel model. I am examining how reaction time varies across weeks (level 1) across years (level 2) across persons (level 3), and I am not sure how to ...
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fuzzy hierarchical clustering

From the Fuzzy C-Means point of view: Every data point belongs to all the cluster which is given by their degree of membership to each cluster. How do I view it in the case of Hierarchical Clustering?...
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What method should I use to cluster small data set?

I would like to cluster a small data sets [23 genes, 50 samples], but I don't know what method I should use... could you give me any recommendation? I have applied hierarchical clustering (Wards ...
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Applying clustering to predicted values

I am using clustering techniques such as hierarchical clustering trees to create an index fund modeled on the S&P500 with the correlation between the returns of individual stocks being used as the ...
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Are all agglomerative clustering methods deterministic?

I'm going over the Agglomerative Clustering algorithm in sklearn.cluster.AgglomerativeClustering. It supports four linkage methods: Ward minimizes the sum of ...
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Interpretation of hierarchical clustered binary dataset

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how would i find number of subtopic inside parent topic dynamically?

Here my objective is to create hierarchy of topic in such a way that sub topics for particular supertopic should represent the same context while it(subtopic) should be isolated with other children of ...
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Best practices in the selection of distance metric and clustering methods for gene expression data

I have been reading about this on various channels including here and Stack Exchange, but I'm still not sure how to choose the best approach for clustering gene expression data. As a Ph.D. molecular ...
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A name/reference for small top-level clusters in hierarchical clustering

When you perform hierarchical clustering, often some outliers "cluster out" at the top level into tiny small-size clusters. I need to discuss this effect in my paper, and I'm not sure how to call it. ...
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Cluster dendogram: does the x-axis has a ordinal/cardinal meaning, or just aesthetic?

It is usual to represent the output of a hierarchical clustering algorithm through a dendogram, which shows in the x-axis the units to be clustered, and on the y axis the cluster dissimilarity measure ...
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What is the way of finding the minimum distance of a merged cluster from other clusters without storing the distance matrix in SLINK by sibson?

In case of SLINK by SIBSON , they say that the time complexity is O(n2) and space complexity is O(n).Now say we have a merged cluster aUb , if we need to find the minimum distance of aUb from any ...
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Are there any examples of differentiable tree or clustered representations of covariance matrices?

There are things like http://proceedings.mlr.press/v5/bravo09a/bravo09a.pdf and various hierarchical methods (see scipy's linkage for example) and Prado's HRP. But I'm wondering if there is a ...
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Using similarity matrix as a correlation input

I am using an approach to decompose sigmoidal signals in R. Briefly, signals are decomposed into a subset of components and then a custom value of similarity is computed among samples defined by 2 ...
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Splits in Decision Trees vs Dendrograms

gradient boosting is a supervised learning algorithm that splits/grows decision trees to improve predictions iteratively. hierarchical clustering is an unsupervised learning algorithm that splits/...
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How the time complexity of the SLINK algorithm by Sibson is O(n2)?

After merging 2 clusters, it computes the distance of that merged cluster from the other clusters, which if repeats n-1 times, the time complexity is O(n3)? I don't understand, whether in SLINK the ...
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Unsupervised soft clustering methods

I have a D-dimensional dataset composed of exactly two clusters (this is known) for which I have no labels; the clusters can potentially be wildly imbalanced. I'm after a soft (or fuzzy) clustering ...
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Model specifications - Independent variables interaction: hierarchy principle

I am testing the effect of commodity demand shocks to the foreign exchange market. Because my hypotheses include three-way interaction effects, I test my hypotheses using hierarchical regression model....
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Any methods for clustering short time series data

I have around 200 different sets of time series data with a semi-annual periodicity. The length of each series varies from 1 to 6 years. I essentially want to build unsupervised clusters by grouping ...
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How to cluster multiple datasets

I've got multiple datasets containing measurements collected at different nodes in the real network. Each dataset is associated with one node. Because some nodes have got similar properties and ...
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Scale the data for cluster analysis

I have a time-series data. I would like to use (hierarchical) cluster analysis for them. I read that I need to scale my data. My question is, can I use empirical function (transfer the data into (0,1))...
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how does VAT algorithm works

I want to know how the VAT algorithm for cluster tendency works in detail. As you can see in the picture below R is the dissimilarity matrix and R-tilda is the ordered dissimilarity matrix. What is ...
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Interaction in Hierarchical Regression

I need some helps to interpret results of a hierarchical regression that included an interaction in the last stage. Dependent Variable is Well-being. Predictors are A-H, as well as the interaction of ...
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Hierarchical clustering: distance/linkage combination that allows starting in the middle of the dendrogram

I want to use hierarchical clustering to classify some ecological data (species abundances on different places), so I would like to use a Manhattan type distance that doesn't account for double ...
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Annotation tool for image clustering ground truth

I'm clustering images, and I'd like to create ground truth cluster labels on a subset. Ideally the process would be something like: Begin with all images in singleton clusters. Find the two closest ...
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Interactive dendogram with plotly and custom distance, linkage functions

I have a matrix X and usually I use scipy to make a dendogram and plot it: ...
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Interpreting the Bk plot in dendextend

So I am trying to compare two hierarchical clusterings of 25 variables. One is my from my "control" population and the other is from a "treated" population and I am trying to see if these populations ...
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Interpreting the Bk plot in dendextend

So I am trying to compare two hierarchical clusterings of 25 variables. One is my from my "control" population and the other is from a "treated" population and I am trying to see if these populations ...
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30 views

composite distance metric for time series clustering

I'm given univariate hourly time series data recorded at multiple meteorological stations across the globe. I'd like to: 1/ cluster these stations according to correlations in the time series - i....
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What are the methods to find the hierarchical relationships between features/variables?

How do I find the hierarchical interrelationships between features/variables? I made a test input file p = 30 and n_samples = n = 569 from a pre-made dataset ...
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Meaningful way to use Dimensional Reduction and Euclidean Distance for clustering variables?

How do I find the hierarchical interrelationships between variables after dimensional reduction since PCA dimensional reduction is sensitive to the ordering of the columns in source data? I made a ...
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How to create clusters in a one-dimensional space that would minimize the variance in the clusters?

We have numeric data in one-dimensional arrays where adjacency matters, e.g. [34,66,87,97,105,43,96] We want to cluster together those values, based on proximity, ...
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Property of a cluster in OPTICS clustering

Using OPTICS algorithm I organized a set of documents, based on their layout, into a hierarchy of clusters. Looking into top clusters, close to the hierarchy root, I can see they contain documents ...
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Geometric significance of the dimensional reduction part of spectral clustering?

While performing spectral clustering of the original data $\{x_1,...x_n\}$, $ x_i\in \mathbb{R}^{d\times 1}$ (column vectors), into $k$ clusters, we Step 1: take the first (smallest) $k$ (column) ...
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K-Means and HCA comparison to a model solution

I’m running several cluster analyses on related datasets and would like to find out which one is closest to the benchmarks/model solutions I would expect based on theory (or otherwise which benchmark ...
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Number of clusters exclusive to hierarchy

I'm working on hierarchical clustering and I stumble on the "where to cut the tree" question. I know there are some methods that can suggest the optimal number of clusters like elbow, gap, silhouette ...
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Increasing multicollinearity in multilevel/hierarchical modeling?

I have a linear model with response variable $\textbf{y}$ and explanatory variable matrix $\textbf{X}$ for which coefficients $\textbf{b}$ are physically meaningful and worth estimating: \begin{...
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Signal/Wavelet Clustering

Problem Setting In an experiment: I have 3 signal sources and 10 sensors each generating wavelets as time goes by. The distances from each source to sensors change from experiment to experiment. ...
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Perform k-means clustering after MCA for transforming categorical variables - provide weights to variables?

I have a very dataset with many observations (> 1 million), with mainly continuous variables and three categorical variables. After searching for clustering methods for mixed data, I decided to ...
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Is it possible to cluster data points based on multiple criteria simultaneously?

I have data points where x and y are its locations and it works well with the usual euclidean-based hierarchical clustering. The image below is the example (I explicitly request how many clusters I ...

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