Questions tagged [clustering]

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

26 questions with no upvoted or accepted answers
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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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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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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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65 views

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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Clustering of very high dimensional data and large number of examples without losing info in dimensions

I'm trying to get a grasp on scalability of clustering algorithms, and have a toy example in mind. Let's say I have around a million or so songs from $50$ genres. Each song has characteristics - some ...
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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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54 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 ...
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82 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 ...
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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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35 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 ...
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21 views

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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47 views

How is clustering used in the unsupervised training of a neural network?

How is clustering used in the unsupervised training of a neural network? Can you provide an example?
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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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18 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 ...
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39 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 ...
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Do Self Organizing Maps (SOMs) require as much data as a typical neural network?

The question is in the title. I'm looking at clustering sequences and have created a short-list of approaches: Clustering on Edit Distance: Needleman-Wunsch: Similarity measure (used in ESAC) ...
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15 views

Are there any well-known ways to fuzzy-cluster (variable length) sequences of trajectories?

I have this issue where I need to create 'soft' clusters for different trajectories. The data is sequences of integers where each integer means a specific point; so I have sequences like $s=(1,47,9)$ ...
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11 views

Dealing with unwanted clustering in the dataset

I have a dataset of sentences that I embedded using the USE for training an image2Seq model. But when I applied t-SNE to the embeddings, applied K-Means, and plotted a scatterplot, I could see that ...
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Split a city into a set of candidate locations (cells) via a clustering algorithm (DBSCAN or OPTICS)

The authors of the following paper predict the market attraction for restaurants based on user reviews for different locations to select an ideal location. To do so they have split the city into a set ...
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50 views

How to use Product Matching to create Product Bundles

I am working on a product matching model. GOAL A store has many products like creams, perfumes, other beauty products. Based on product properties I have to create bundles of it so we can sell more ...
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Interpreting a self organizing map resulting from the same dataset with different standard deviations

I am working on a task where the goal is to make a sofm learn a mapping from a three-dimensional space (the input space) to a two-dimensional space (the sofm "grid"). The data points are ...
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28 views

ML method for detecting which individuals are best predicted by the features

Broad question to help in finding an appropriate method. So I have a given feature set of (DNA/genetic) predictors and a group of individuals which are either cases or controls for diseaseX. While I ...
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Deep unsupervised clustering on big data with no prior knowledge

I have around 3 million BW images. I would like to organize them in as few clusters as possible in a way that is meaningful for the dataset without any prior knowledge for this data, as they come from ...
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30 views

Binary data clustering by Matrix factorization

I have read an article talking about binary clustering using Matrix factorization(see attached), but i would like to understand some optimization concepts: Is it reasonable to use a Frobenius norm in ...
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57 views

How can I group the entries of the network traffic by their similarity?

I have the traffic of my network (with hundreds of entries). Below I am showing only 9 entries. ...
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58 views

How can I cluster based on the complementary categories?

K-means tries to find centroid and then clusters around the centroids. But what if we want to cluster based on the complement? For example, suppose we have a group of animals and we want to cluster ...