Questions tagged [clustering]
For questions related to clustering (a usual unsupervised learning technique).
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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 ...
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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 ...
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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.
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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 ...
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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 ...
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Clustering of Graph with Binary Vertex Labels
Consider a graph data structure with unweighted edges, where each vertex has been assigned either 0 or 1.
I am wondering if there exists a good way of clustering this graph to detect communities. All ...
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What is the difference between "Image Clustering" and "Unsupervised Classification" tasks?
I am trying to compare some results that I obtained in benchmarks with my unsupervised model. My model basically takes an unlabelled dataset and clusters it into semantic classes (10 clusters in the ...
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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
...
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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 ...
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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 ...
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How does FaceNet (or similar) bootstrap new faces?
In a metric learning system the system can be trained on known examples such that common classes (faces) are clustered together and separated from each other as much as possible. If triplet loss is ...
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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 ...
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Should I expect the FaceNet to learn to group faces that look different, but in a (probably) predictable way?
A FaceNet learns to cluster images containing the same face together. I want to use a pre-trained FaceNet that was trained to do this, to now learn to cluster faces together, thus clustering clusters ...
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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 ...
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Classify/categorize parts of text with machine learning (Python)
I need to parse some documents (which consists of questions, answers, transition messages and other texts) into a structured format. Here is an example of input document (part of it):
I have to ...
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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=...
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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,...
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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) ...
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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 $d$ ...
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241
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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
...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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How to handle list features in clustering?
I have a dataset where one of the features is a list.
Example:
...
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281
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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 ...
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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 ...
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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 ...
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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 ...
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103
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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 ...
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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 ...
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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.
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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 ...
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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 ...
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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?
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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 ...
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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 ...
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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}...
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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 ...
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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 ...
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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 ...
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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?
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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 ...
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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 ...
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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 ...
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How can I cluster this data frame with several features and observations?
How can I cluster the data frame below with several features and observations? And how would I go about determining the quality of those clusters? Is k-NN appropriate for this?
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Neural network to extract correlated columns
I want to use a neural network to find correlated columns in a .csv file and give them as a output. The input .csv file has ...
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Finding unique faces in a video
I am trying to find unique (distinct) faces in multiple videos files. What is the best way to do that?
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