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.]
1,310 questions with no upvoted or accepted answers
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What approaches use multiple eigenvectors in graph spectral clustering?
Background: In Newman's PNAS 2006 paper Modularity and community structure in networks, the first eigenvector splits the graph in two clusters, and then each cluster can be further divided by ...
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Cluster analysis vs Factor analysis as a means for "grouping" variables or cases
I've noticed responses that at face value seem to be in contradiction with each other.
For instance, here @peter-flom writes
Short answer: Cluster analysis is about grouping subjects (e.g.
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Territories from observations
I have a number of animal observations, and want to deduce the number of territories (i.e. the number of individual animals) from this.
More formally, the problem can be stated as follows: Each ...
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How to predict routes using clustering data
I've been working on a ship route prediction algorithm such that given the past and current trajectory of a ship I am able to estimate the future one. The trajectories are represented as a sequence of ...
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Time series clustering: Fourier transform and PCA
I have biological time series (9 years long) of the biomass of species which logically exhibit a seasonal pattern. I would like to cluster them into a few groups based on their typical seasonal ...
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Regarding the size of training data for building classifier
When we build a classifier, like SVM or Naive Bayesian, are there any generic rules or theoretical derivations on the size of training data set? For example, to train a SVM-based classifier, what ...
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Compare clustering results with different attributes and number of clusters
I used K-means to cluster a large data set that has millions of samples. I tried to create the clusters with different sets of attributes, which, as a result, generated different optimal number of ...
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Correct number of components in GMM according to BIC and AIC plots
I have applied GMM(Gaussian Mixture Model) to my data set and I have plotted the resulting BIC(Bayesian Information Criterion) and AIC(Akaike Information Criterion) for different number of components. ...
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Interpreting Silhouette plot
Can someone help me interpret this silhouette plot?
The things that come up on my mind are:
Some clusters are very small
Orange cluster is very big
Pink, dark green and light green clusters are ...
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Is K-Medoids really better at dealing with outliers than K-Means? (with example showing the contrary)
K-Medoids and K-Means are two popular methods of partitional clustering. The consensus is that K-Medoids is better at clustering data when there are outliers (source). This is because it chooses data ...
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Unsupervised clustering with "unclassified" items
I have data (some behavioral features, measured on some scales) on people. I want to cluster people based on these features. This is an unsupervised scenario, as I have no prior knowledge on the ...
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Detecting statistically significant clustering of continuous values
I'm working with biological sequence data where each position in the sequence has an associated continuous value. I'm ignoring the sequence content so the data is very similar to a time series with ...
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Using Davies-Bouldin index in clustering
I am clustering data using k-medoid. I used Davies–Bouldin index for $2$ to $n-1$ clusters. Here $n = 100$ (using smaller test case). I find minimal value of the index for 98 clusters. But the overall ...
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In which case does FCM membership converge to 1/K?
I have tested the fuzzy C-means (FCM) algorithm using the R function fanny from the cluster package and I have wrote my own FCM ...
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Python Implemenatation of SPSS's Two-Step Clustering
I want to perform a clustering on data with ~40 binary features. I was recommended the two-step approach by Chiu et al.. They basically use a BIRCH variant to determine pre-clusters and then perform ...
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Clustering text embeddings: TF-IDF + BERT Sentence Embeddings
I am trying to cluster a few thousand forum posts that are similar in content to Stackoverfow.
So far, I have tried two main approaches to represent the posts:
TF-IDF
Sentence embedding based on BERT....
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Can clustering with Gaussian mixture models be done based on cosine similarity?
Apologies if this has already been answered; I found some similar posts (here and here) but don't feel they answered the specific question I have. Please feel free to correct any misunderstandings in ...
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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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Missing data in k-means cluster model
I'm working on clustering email addresses using K-means based on their value to and engagement with the company (metrics such as % of emails opened, # of web browsing sessions, etc). I would like to ...
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A graph-based clustering problem
I have a graph in which each node is associated with a time stamp. I have around 15-20 nodes associated with each time stamp.
The edges are not weighted & there cannot be an edge between nodes ...
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How to determine the best batch-size value for Mini Batch K-means algorithm?
I am working on a project where I apply k-means on severals datasets. These datasets may include up to several billion points. I would like to use mini batch k-means to save time. However, the mini ...
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What is the probability of forming a cluster of the size M given N random points?
Suppose we generate a sample of uniformly distributed numbers of the size $N$ in the range $[0, 1]$: $x \sim U[0, 1]$. We consider that if the difference between any 2 numbers is smaller than some ...
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Gradual clustering in deterministic manner
We have 128-dimensional vectors representing people's identities where the euclidean metric defines the similarity between them.
Ours solution requires them to be clustered and then annotated (assign ...
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Clustering Markov Chains
Assume I have a finite set of elements
$$A={X_0,X_1,...,X_{n_1}}$$
where every element is itself a finite Markov sequence (i.e. $S_{i+1} \sim P(S_i)$)
$$X_i={S_0,S_1,...,S_{n_2}}$$
Suppose I also ...
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Pearson's Correlation Coefficient as a clustering criterion: why should it be close to -1?
Let's say we have a dataset $x = (x_1, x_2, ..., x_n)$ where each data point is assigned to one of $m$ clusters.
Let $D = \{d_{ij}\}$ be the $n\times n$ distance matrix where $d_{ij} = d(x_1, x_j)$.
...
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Why does Kernel K-means work better than spectral clustering in this case?
I want to cluster a dataset using spectral clustering. Assuming $X$ is $d \times n$ data matrix as $n$ is the number of data samples. I construct a directed Adjacency matrix $W, n \times n$ in which ...
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Bag of Features / Visual Words + Locality Sensitive Hashing
PREMISE:
I'm really new to Computer Vision/Image Processing and Machine Learning (luckily, I'm more expert on Information retrieval), so please be kind with this filthy peasant! :D
MY APPLICATION:
...
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Topology of Confidence Intervals
I hope this is the right site to post this.
The example I have in my mind is a GLMM model, where we infer random effects, and a random effect caterpillar plot (with confidence intervals):
Now, ...
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A high cophenetic correlation coefficient but dendrogram seems bad
I have 2 results for the same dataset. One is hierarchical clustering using Ward's method and I got 0.75 cophenetic correlation coefficient. The second is average method and I got 0.91 cophenetic ...
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Interpreting silhouette coefficient for clara function in R
I am trying to do clustering on a distance matrix which contains numeric data. But I am not sure how to decide upon the number of clusters or value k for clara function in R. But after running it with ...
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Elbow method or the Silhouette to determine number of clusters
I would like to know what is the better way to determine the number of clusters - elbow method, or the silhouette?
I've used elbow method, increasing number of clusters while the total distance ...
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Checking the assumptions of K-means clustering
I want to do a k-means clustering on a dataset containing 22 numerical variables between 0 and 100 and 75 observations using R. I read this post
How to understand the drawbacks of K-means on k-means ...
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watershedding vs mean shift clustering
I'm working on a clustering problem, and I was trying to understand the difference between the watershed algorithm and mean shift clustering. It seems that these algorithms are popular for image ...
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Stability of Time Series Hierarchical Clustering
We have a dataset with six time points and three biological replicates each. Therefore, we have a vector of 18 measurements for each feature, and used hierarchical clustering with Euclidean distance ...
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Clustering Consumer data with over 100 variables and 50000 rows each
I am tasked with performing a clustering exercise for a consumer survey dataset with the hopes of finding distinct consumer segments.
In the past, I've done it using a variety of techniques- ...
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Use of Hidden Markov Models for Clustering
I would like to ask whether Hidden Markov Models can be used for clustering and if so, in what cases.
I have found somewhere, references like this but practically I haven't found a way to do this. Is ...
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Spatial cluster analysis
Let's say I have a structure like this :
This is a spatial region with measurement of plant population in each site. Black and red represent two regions with different intensities.The question is ...
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Maximam r distance for Ripley's K-function
I am using R's package spatstat to study the locational pattern of conflict events in Africa (around 8.000 points) using point pattern analysis techniques.
I was able to obtain the plot of $g(r)$, ...
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Which groups of results is the closest to a central point?
I'm building an application where a specific location is chosen, multiple services are polled to return results for that specific location and shown on a map.
I have the results from the different ...
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Which variables are driving correlations within groups
I'm running an analysis on a few data sets that each typically have 100-200 cases measured across 120-160 variables - something similar to looking at gene expressions. Each variable is a non-centered ...
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Cluster on high dimensional categorical data (Images with keywords)
We're looking for clues to perform a Cluster Analysis in a DB with +400K images which have keywords associated to them.
Each image could have from 1 to 30 keywords.
Total keywords count is +35K.
...
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What are pitfalls of bootstrapping on random sample of master data?
Will I obtain seriously biased results if I use bootstrapping on a subsample of a larger dataset?
Rather than drawing 100 bootstrap samples from a dataset of 50 million + records, which could hog ...
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Category selection for text classification
It is said that to achieve a good result (many different metrics) for text classification, it is not always a business of selecting the algorithm/classifier. Sometimes, it is even more important to ...
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Is dimensional reduction using Autoencoders possible with a small sample size?
I have a data set that is not too big but high dimensional, let say 10000 dimensional. I want to use an autoencoder to extract relevant features (clusters) in the data. Usually when I have seen ...
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Clustering and grouping of rhythmic data (acrophases)
I am looking for suggestions towards a clustering method for rhythmic/oscillation data.
We performed cosinor regression (sin(2*pi*time/period)+cos(2*pi*time/period))...
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Test for comparative deviation from a random distribution between two clusterings
I have a question related to clustering comparison and biostatistics.
I performed 2 clusterings (let's call them A and B) on a large gene set, so for each gene I have 2 cluster attributions. each ...
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Cluster Algorithm for multidimensional data
My goal is to cluster data (20000 samples with a range from 0.0 to 1.0, and 14 dimensions/features). Since I don't know the number of clusters, I tried using MeanShift and DBSCAN.
My problem with ...
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Does the Membership Matrix of Fuzzy C-Means Clustering contain probabilities or degrees of membership?
I recently heard a lecture on Fuzzy C-Means Clustering that stated that the Membership Matrix contains probabilities that particular data points are members of particular clusters. I was confused by ...
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Is there an MDS/embedding algorithm that is more suitable to the goal of clustering a graph
I am testing ideas on clustering a particular graph. After testing a set of graph clustering/community detection algorithms I thought about mapping the graph to a vector space and using vector space ...
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Clustering large set of images
I've got some big datasets of images (a few million each), and I would like to cluster them according to images' visual similarities. I've extracted a feature vector for each image; the space of ...
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