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Questions tagged [clustering]

For questions related to clustering (a usual unsupervised learning technique).

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How does the Clustering algorithm comes up with the name for each cluster?

Currently I'm studying different types of of machine learning and I came across the subject Clustering....As I understand clustering identifies similarities between observations based on their ...
Brandon Michael Hunter's user avatar
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Clustering: which correlated feature to eliminate

I am trying to perform a simple k-means clustering of a sample population based on about 30 features. Some of the variables are highly correlated as measured by the Pearson correlation coefficient and ...
SAS2Python's user avatar
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How to classify a new node into an existing cluster of nodes?

I have a graph with many disjoint subgraphs that are not connected to each other. Essentially these subgraphs could represent different clusters. What is a general process to figure out node ...
Kiran Manicka's user avatar
2 votes
1 answer
68 views

What does "aligned" across domains in domain adaptation?

within Delving into Local Features for Open-Set Domain Adaptation in Fundus Image Analysis paper. I got trouble in understanding their cluster-aware contrastive adaption $\mathcal{L}_\text{cca}$. I ...
chickensoup's user avatar
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Improving Neural Network Model Accuracy with Initial Poor Classifications and Clustering

I have a dataset consisting of 52 files, each classified as 'yes' or 'no' for 22 different attributes based on specific subparts, not the entire content. After tokenizing, converting non-generic ...
Ferda-Ozdemir-Sonmez's user avatar
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1 answer
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How can I combine unsupervised learning with supervised learning?

I am currently using an isolation forest (from sklearn library) to detect anomalies in a data frame (basically it's a dynamic data frame more of a kind of time series I am. But I have certain criteria ...
SUNITA GUPTA's user avatar
1 vote
0 answers
17 views

Looking for a way to train a model to learn optimal parameters/hyperparameters of clustering

I have 5000 docs, each is a review. For each review, i'm plotting the sentences in a semantic dimension. Now, I'm applying clustering to these points for each review. The success of my model depends ...
Prithvi's user avatar
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I have n-dimensional latent representational data, with a y logit label: How do I find peaks in the data using the label?

I essentially need a "find peaks" algorithm for when the input data is n-dimensional. Specifically, in the latent space of my neural network I have have collected all the training data ...
Eoin Ó Coinnigh's user avatar
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Algorithms for community discovery in multigraphs

In order to group unstructured or sem-structured texts for a timeline construction approach, I consider several types of correlations among such texts. These different correlations induce a weighted ...
Max Muller's user avatar
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Distance functions used in clustering analysis

From what I've seen in clustering, distance is taken as a hyper parameter (which is to be selected) when inferring the relationships/clusters between points. Examples of distances I've come across ...
ABIM's user avatar
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How to check clustering performance?

Background I'm implementing the DBScan algorithm. I have trained it to cluster a small dataset of random clusters, and want to be able to get a decimal for its accuracy of clustering the groups. ...
SamTheProgrammer's user avatar
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Mining overlapping orthogonal clusters from high dimensional data

What are the recommended strategies for identifying overlapping and somewhat orthogonal clusters in a large, high-dimensional dataset? As an illustration, consider a dataset comprised of various ...
curl-up's user avatar
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1 answer
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How to learn Categorial Embeddings in Unsupervised Learning?

I want to cluster mixed-type tabular data, for the categorial columns I want to use Categorial Embeddings and then an Autoencoder Network before clustering with KMeans or similar. Now, when I want to ...
Jaanis's user avatar
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1 vote
0 answers
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Clustering key, value pairs with similar meanings

I have a dataset of key-value pairs that describe specific medical laboratory mappings. Example of this data: Group Name Specific Test Name Blood Test Red Blood Cells Blood Test Hemoglobin ...
DemCodeLines's user avatar
1 vote
0 answers
15 views

Looking for scalable spatiotemporal clustering algorithm or scalable mixed features data clustering algorithms

I want to cluster the Porto taxi dataset from https://www.kaggle.com/competitions/pkdd-15-taxi-trip-time-prediction-ii/data . After processing the dataset, I have a dataset which consists of temporal ...
I am not a robot's user avatar
1 vote
0 answers
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Clustering by number of spacial nodes in an area

I have a huge a amount of coordinates (hundred of millions) and I want to group them in areas, where every area should have approximately the same number of nodes and no more than a limit. I was ...
angelcervera's user avatar
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1 answer
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Pre-Trained Model for Occupational Coding

I've recently embarked on a task to classify an occupation code, given a job title & description. I have come across clustering, a method of grouping data into clusters of which were not ...
Ryley Keegan's user avatar
1 vote
1 answer
941 views

How to get meaningful vector embeddings for (lat, long) points and also GPS trajectories?

I have a data that consists of approx. 1.5M taxi trips in Porto, Portugal. (from: https://www.kaggle.com/competitions/pkdd-15-taxi-trip-time-prediction-ii/overview) Each of these trips have it's GPS ...
I am not a robot's user avatar
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1 answer
71 views

Segmentation of x-ray images to detect Covid-19

I’m currently working on covid detection project using x-rays. I applied K -means clustering algorithm (https://www.kaggle.com/code/naim99/image-classification-clustering-step-by-step?scriptVersionId=...
S i's user avatar
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1 vote
1 answer
377 views

Interpretation of the Dynamic Time Warping (DTW) graph

How can I interpret ate the DTW graph. I understood the algorithm behind DTW, but I struggle to interpret ate the graph. When I compute the DTW for a signal that is the same signal but shifted in time,...
Skobo Do's user avatar
1 vote
1 answer
72 views

Deep Clustering Approach for Unsupervised Video Anomaly Detection

I'm working on Unsupervised Video Anomaly Detection, and I've tried implementing the Generative Cooperative Learning method, with the help of this paper. The method uses a fixed backbone (ResNext-101) ...
satan 29's user avatar
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1 answer
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Clustering by using Locality sensitive hashing *after* Random projection

It is well known that Random Projection (RP) is tightly linked to Locality Sensitive Hashing (LSH). My goal is to cluster a large number of points lying in a $d$-dimensional Euclidean space, where $d$ ...
Penelope Benenati's user avatar
1 vote
1 answer
352 views

Feature Engineering on transactional dataset clustering

I have a data set with transactions details from different business (roughly 1 thousand business entities). Each row is a transaction. The structure of the dataset is as follows: client_id Sex Age ...
Juan Ignacio Rojo's user avatar
1 vote
1 answer
145 views

How do you evaluate a k-medoids cluster model?

So I'm planning on clustering a bunch of observation data using k-medoids. There are seven attributes for each instance and the data is numerical and discrete. I'm a little uncertain of how to ...
UntilComputersTakeOver's user avatar
2 votes
1 answer
58 views

How to tackle the human error made in labeling datasets for classification tasks like facial expression recognition?

I am working on the Facial Expression Recognition Task. One of the most challenging tasks that I faced was human error in labeling the datasets (ex: let's say FER2013). Are there anyways to Handle ...
Dilip C M Dept of MCA's user avatar
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Could I cluster the audio clips in order to improve the speed of their classification?

I have a neural network which is very resource intensive and is used to classify audio clips. The classification is done in batches, where I record for a set period of time and then go through and ...
GILO's user avatar
  • 101
2 votes
1 answer
60 views

What clustering algorithms work best for datasets with only binary categorical features?

I have a dataset with a lot of binary categorical features and a single continuous target value. I would like to cluster them, but I am not quite sure what to use. In the past, I have used DBSCAN for ...
user199590's user avatar
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1 answer
58 views

Is there a term for unquantifiably uncertain prior knowledge?

I'm working on a clustering algorithm which assigns each data point an index encoding its cluster. Index permutation is irrelevant to the correctness of the result. The algorithm is self-learning, in ...
programonkey's user avatar
1 vote
1 answer
111 views

What happens if all the features are correlated with each other before clustering?

I know that when two features are highly correlated with each other, one of them should be removed from the dataset so they don't add twice the weight. However, what if all my features share a ...
Sanzor's user avatar
  • 113
0 votes
1 answer
31 views

How to handle list features in clustering?

I have a dataset where one of the features is a list. Example: ...
Bob Sacamano's user avatar
2 votes
1 answer
344 views

Perform clustering on high dimensional data

Recently I trained a BYOL model on a set of images to learn an embedding space where similar vectors are close by. The performance was fantastic when I performed approximate K-nearest neighbours ...
VEDANT JOSHI's user avatar
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0 answers
116 views

What is the best machine learning algorithm for clustering dots based on coordinates $(x,y)$ with consideration of weight of the points?

I'm looking for a machine learning algorithm for clustering points based on their coordinates. Furthermore, I want to take into consideration the weights of each point. Suppose there is a weight in ...
chung sze wong's user avatar
2 votes
0 answers
107 views

What would be a reasonable option for clustering for unknown number of clusters and a lot of outliers?

I am implementing the CV detection pipeline with the use of SIFT and KNN Matcher. Image keypoints matched to the query keypoints produce the following image: The matched objects have a lot of key ...
spiridon_the_sun_rotator's user avatar
0 votes
1 answer
121 views

In this example of fuzzy c-means, what is the difference between "sigma" and "center" for the clusters?

In this example, what exactly do "Cluster" and "Sigma" mean? (They chose random coordinates for the three centroids of the groups) Centers: Cluster centers, returned as a ...
user5520049's user avatar
0 votes
2 answers
153 views

How to reduce the number of clusters produced by the Markov Clustering Algorithm?

I have used the Markov Clustering Algorithm (MCL) to cluster tweets, based on their similarity. However, I got a too high number of clusters, and most of the clusters have only one tweet. Any ...
Adnan Hussein's user avatar
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0 answers
60 views

What are some machine learning frameworks for supervised clustering?

I have a task where I need to take "data points" which consist of collections of items. Each item needs to be categorised according to predefined categories. That's the easy part - my ...
Alexander Soare's user avatar
1 vote
0 answers
22 views

What is the best clustering method to detect anomalies for data with mostly categorical data?

I have a dataset with about 85 columns. Out of the 85 columns, 70+ are categorical. My goal is to identify the outliers in this dataset through clustering methods as I do not have a target column. ...
user13074756's user avatar
1 vote
0 answers
38 views

Is there a technique for analyzing the relationship between time-series clusters?

I have two time-series datasets (temperature and speed of the vehicle). I will use Agglomerative Hierarchical Clustering and DTW to cluster both datasets. I am looking for a technique (like regression ...
Asa Ya's user avatar
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2 votes
1 answer
103 views

Is there a clustering algorithm that can make n clusters and the n+1 "others" cluster?

As far as I know all clustering algorithms assume that all delivered data points have to find its cluster. My question is, is there an algorithm that could focus only on n clusters (number stated by ...
GKozinski's user avatar
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4 votes
1 answer
186 views

How to define machine learning to cover clustering, classification, and regression?

How to define machine learning to cover clustering, classification, and regression? What unites these problems?
Marina's user avatar
  • 171
4 votes
2 answers
93 views

Which metric should I use to assess the quality of the clusters?

I have a model that outputs a latent N-dimensional embedding for all data points, trained in a way that clusters data-points from the same class together, while being separated from other clusters ...
jaeger6's user avatar
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2 votes
1 answer
154 views

What exactly is the eigenspace of a graph (in spectral clustering)?

When we find the eigenvectors of a graph (say in the context of spectral clustering), what exactly is the vector space involved here? Of what vector space (or eigenspace) are we finding the ...
Manish Kausik Hari Baskar's user avatar
3 votes
0 answers
32 views

What is meant by subspace clustering in MFA?

The basic idea of MFA is to perform subspace clustering by assuming the covariance structure for each component of the form, $\Sigma_i = \Lambda_i \Lambda_i^T + \Psi_i$, where $\Lambda_i \in \mathbb{R}...
stoic-santiago's user avatar
2 votes
0 answers
86 views

How does Hartigan & Wong algorithm compare to Lloyd's and Macqueen's algorithm in K-means clustering?

As far I know, this is how the latter two algorithms work... Lloyd's algorithm Choose the number of clusters. Choose a distance metric (typically squared euclidean). Randomly assign each observation ...
Arif Al Hashmi's user avatar
1 vote
0 answers
62 views

How to cluster data points such that the number of clusters is kept minimal and each cluster projects well onto a lower-dimensional subspace?

If I want to find a (linear) subspace onto which a data-set projects well, I can simply use PCA. However, often the data can project with much smaller error if I first separate it into a couple of ...
matthias_buehlmann's user avatar
1 vote
0 answers
93 views

Combining clustering and deep learning for computer vision

Is there any recent work on combining clustering approaches (k-means, or gaussian mixture or PGM) with deep learning for computer vision? In particular I'm interested in if anyone has used the first ...
FourierFlux's user avatar
2 votes
0 answers
99 views

Would it be possible to implement the principals of the K means clustering algorithm in a Neural Network

During a Machine Learning course which I have done I have learnt about the K means algorithm. Is it possible to use the principals of K means within a neural network?
jr123456jr987654321's user avatar
2 votes
1 answer
716 views

Is this dataset with only two features suitable for clustering with k-means?

I am working with the K-means clustering algorithm for unsupervised learning. Is the following dataset suitable for the k-means clustering task or not? Why or why not? The dataset has only two ...
Debugger's user avatar
1 vote
0 answers
39 views

How do I approach this problem?

Let's say I have a dataset with multiple types of multiple ingredients (salt1,salt2, etc). Each n-th variation of each ingredient vs flavor may be represented by an n×k matrix that where an ingredient ...
mightychrysanthemum's user avatar
2 votes
2 answers
173 views

Could clustering be used to parse pdf documents to get headings and titles?

I'm a bit new to AI and I'd like to use some kind of clustering algorithm to solve a problem: I'm trying to parse pdf documents to get headings and titles. I can parse pdf to html and I'm then able ...
Wylex's user avatar
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