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.

Filter by
Sorted by
Tagged with
0
votes
0answers
16 views

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 ...
2
votes
1answer
26 views

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 ...
1
vote
0answers
18 views

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 ...
0
votes
0answers
7 views

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 ...
0
votes
0answers
14 views

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: ...
0
votes
0answers
19 views

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 ...
0
votes
0answers
11 views

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 ...
0
votes
0answers
13 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....
1
vote
0answers
19 views

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 ...
0
votes
0answers
39 views

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 ...
1
vote
0answers
30 views

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, ...
0
votes
0answers
9 views

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 ...
1
vote
1answer
31 views

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) ...
0
votes
0answers
29 views

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 ...
1
vote
0answers
19 views

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 ...
5
votes
1answer
48 views

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{...
0
votes
0answers
10 views

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. ...
0
votes
0answers
57 views

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 ...
1
vote
0answers
15 views

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 ...
1
vote
1answer
26 views

Clustering - Different algorithms, same results

I'm working on my first clustering assignement and I've ran K-Means, Spectral clustering, Hierarchical clustering and Mini-Batch K-Means on same data and received the exact same results (cluster sizes,...
0
votes
1answer
38 views

Multilevel Modeling: Clustering by both individual and time, is this okay?

I'm trying to run a multilevel model where I have approximately 30 individuals and anywhere from 20-50 time points per individual. I can cluster them by the individual since the dataset is ...
0
votes
0answers
23 views

Ward's method for calculating linkage

I have this points in 1d: $$x_1 = 1$$ $$x_2 = 1.5$$ $$x_3 = 3$$ $$x_4 = 4.5$$ $$x_5 = 8$$ $$x_6 = 9$$ And I need to do a dendrogram by hand using Ward distance. I wanted to verify my results so I ...
1
vote
0answers
30 views

What is the intuition behind centroid linkage in hierarchical clustering?

In the sample code below I would expect a tree with 3 branches if it was really using centroids. Indeed the 2 closest points are A and B with a distance of 1 < sqrt(5)/2. The centroid of cluster (A,...
0
votes
0answers
27 views

Hierarchical Clustering: Extract observations from large heatmap

I'm currently trying to visualize a large data set as heat map. That in itself works smoothly but I struggle with gaining insights from interestingly looking clusters. Specifically, I have two ...
0
votes
1answer
15 views

R: Clustering data of mixed types when N is large (1+ million rows)

I seem to be stuck trying to cluster my data of 1+ million rows, with columns of both continuous (e.g. age) and categorical (e.g cats, dogs, birds) types. It looks like daisy() from the cluster ...
0
votes
1answer
38 views

how to scale a Poisson-like distribution for cluster analysis?

I want to perform cluster analysis based on three values for each observation. All these values are extremely skewed to the right. they are the number of friends, followers, and statuses of a person ...
1
vote
2answers
88 views

Can Agglomerative Clustering (Hierarchical) form non-convex clusters?

I want to know whether Agglomerative Hierarchical clustering draws non-convex cluster boundaries. From sklearn's comparing diff clustering algorithm experiment it seems like Agglomerative clustering ...
1
vote
1answer
25 views

Correct clustering approach for segmenting stores

Domain : Retail I have a set of stores which I want to cluster into similar stores based on 10 variables: revenue, avg income, market share etc. I took 2 approach: Approach 1: Given there are 10 ...
0
votes
0answers
7 views

Save scikit agglomerative clustering iterations

I'm using the scikit-learn module of agglomerative hierarchical clustering to obtain clusters of a three million geographical hexagrid using contiguity constraints and ward affinity. My question is ...
0
votes
1answer
45 views

In cluster analysis, is it better to normalize the data or standardize it?

In cluster analysis, is it better to normalize to $[0, 1]$ (i.e., $\frac{x-\min(x)}{\max(x)-\min(x)}$) the data or standardize via z-score (i.e., $\frac{x-\bar{x}}{s_x}$) it? I know normalization ...
0
votes
0answers
11 views

Using dimension reduction techniques for poverty/wealth indicator

I would like to create an indicator/index of a person's wealth (or socio-economic status, SES). I have about 20 variables that are a combination of education, household assets, access to money, and ...
0
votes
0answers
40 views

How can I choose the best result based on clustering?

I have a question after working with the results of the following question: Plotting a heatmap given a dendrogram and a distance matrix in R. I have worked with the second answer, and I used the ...
0
votes
3answers
44 views

How to identify or give a meaning to the cluster membership in a hierarchical clustering?

I know clustering is a type of unsupervised learning problem, however when Kmean clustering is used one can sort the membership based on the cluster centers. For example consider the cluster ...
0
votes
0answers
90 views

Using NbClust on datasets that produce some negative eigenvalues. When to exclude data, when to force to positive, when to exclude test index?

Background on why I am using clustering: I am analyzing data from a multistep biological experiment, where each step is done in batches of varying sizes. I want to account for any biases that might ...
0
votes
0answers
18 views

R: Attribute Overepresentation in a cluster

I performed hierarchical clustering in a dataset with 4 categorical variables (2 nominal, 2 ordinal) in R using hclust and daisy ...
1
vote
1answer
97 views

Should we most of the time use Ward's method for hierarchical clustering?

By browsing notebooks on the web, I see that most of the time Ward's method is used for hierarchical clustering. What could explain its popularity? Does it mean that in general it performs better than ...
0
votes
1answer
83 views

Hclust in R - Categorical data with multiple categories

I have a dataset with 266 observations with categorical variables of multiple categories. I am using the function hclust in R and the function ...
1
vote
1answer
75 views

Am I right that Calinski-Harabasz index (Pseudo-F) can not be calculated from a distance matrix other than euclidean?

Part: I wonder if one could calculate the Calinski-Harabasz index when only having a distance matrix (and a cluster solution, of course). As you need the within and between sum of squares to come up ...
0
votes
1answer
45 views

distance metric for student course schedules

I'm doing an exploratory clustering analysis of student course schedules at a college. Interpretability by humans is paramount: we're trying to inform future research questions and possibly ...
0
votes
0answers
61 views

Evaluating HCPC clusters using cluster.stats from fpc library

I'm trying to get quality measures for example silhouette from HCPC clusters I can get avg.silwidth from the k-means algorithm, using cluster.stats from fpc library : ...
0
votes
1answer
100 views

Suggestions to cluster more than 300k observations

I am trying to perform an hierarchical clustering on data frame that contains 300k records 7 features (3 binaries and 4 continuous) in order to get insights on what looks like my dataset. I've chosen ...
1
vote
2answers
83 views

Hierarchical Clustering, Why Always Agglomerative?

I'm working on clustering for 6 month right now and there is question that bothers me lately, and that is why in every single resource about hierarchical clustering someone introduced two types of it (...
1
vote
0answers
53 views

How does one apply hierarchical agglomerative clustering when multiple positions are equidistant (non-unique distance matrix)?

I have been following this single/minimum -linkage example to better understand hierarchical agglomerative clustering. I noticed that the entries of d_ij are unique ...
1
vote
0answers
60 views

Hierarchical clustering on principle components for multidimensional scaling

Essentially I have a data set of distant objects in which I've loaded onto factors using a multidimensional scaling technique. From my understanding, the factor loadings only differ between MDS and ...
0
votes
0answers
12 views

Stepwise regression approach to clustering with “both” direction (merges and splits)

Classic approaches for clustering a set of points are either top-down or bottom-up. At high level: Top-down (or Divisive): you start with a single cluster, then split a single point or a bunch of ...
0
votes
1answer
322 views

Difference between agglomerative and divisive clustering in terms of results?

Both methods in Hierarchical clustering have always the same result (number of clusters and instances in the same clusters) and the difference is only the way they use to compute the result? Or the ...
0
votes
0answers
45 views

Is it possible that PCA works better without data scaling? [duplicate]

I am a beginner. I have a dataset of 1700 samples with 4 features and I have to perform Hierarchical Clustering (the agglomerative version) and I need to decide whether or not to scale the data and ...
0
votes
0answers
119 views

OPTICS Reachability Curve: Scikit-learn VS PyClustering implementations

I am comparing the Reachability curves generated by the OPTICS algorithm using the Scikit-learn (new implementation) VS the PyClustering one. The hyper parameters are not the same, but it seems that ...
1
vote
1answer
38 views

How does non-uniqueness of data (aka duplicate data points) affect clustering?

I am trying to self-learn more about different clustering methods. I think I understand the main idea of the algorithms, but perhaps their use-cases can shed light on something that puzzles me - ...
0
votes
0answers
73 views

What does a high silhouette score for assigning everything to 1 cluster mean?

I'm writing my bachelor's thesis and I'm running into an oddity. When running k-means and hierarchical, the clustering is fairly evenly distributed - there isn't a clear preponderance of data points ...

1 2 3 4 5 7