Questions tagged [clustering]

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

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12 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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17 views

Write $k-$Means as a Genetic Algorithm (Genetic Clustering Algorithm) [closed]

I'm trying to write $k-$Means a genetic algorithm but i'm having trouble with the chromosome representation of the algorithm. I'm thinking in represent $k-$Means as a string (chromosome) with $n$ ...
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14 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. ...
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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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34 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 ...
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7 views

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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1answer
47 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 ...
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44 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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77 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?
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31 views

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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2answers
40 views

Accuracy metric for Clustering

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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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 3M BW images and I would like to organize them in as few clusters as possible in way which is meaningful for the dataset without any prior knowledge for this data, as they come from ...
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1answer
65 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 ...
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18 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}...
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45 views

Deep Continuous Clustering algorithm - just one output cluster

I use the DCC algorithm to cluster some data. The whole algorithm is available here, but shortly it is: construct mkNN graph of the data points (the connected components of it are the clusters). ...
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28 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 ...
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29 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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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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80 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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61 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?
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1answer
117 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 ...
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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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56 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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2answers
61 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 ...
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6answers
74 views

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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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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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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46 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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39 views

How to detect patterns in salary distribution if we are suspecting malicious distribution based on employee's region?

We are having suspects in salary distribution in our organisation due to employee's region. The data we have is as the following: ...
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21 views

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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1answer
116 views

Is unsupervised learning a branch of AI?

From Artificial Intelligence: A Modern Approach, a book by Stuart Russell and Peter Norvig, this is the definition of AI: We define AI as the study of agents that receive percepts from the ...
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2answers
53 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 ...
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1answer
437 views

What is the role of the 'fuzzifier' w in Fuzzy Clustering?

According to my lecture, Fuzzy c-Means tries to minimize the following objective function: $$J(X,B,U)=\sum_{i=1}^c\sum_{j=1}^n u_{ij}^w \, d^2(\vec{\beta_i},\vec{x_j})$$ where $X$ are the data ...
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2answers
335 views

How to compute the number of centroids for K-means clustering algorithm given minimal distance?

I need to cluster my points into unknown number of clusters, given the minimal Euclidean distance R between the two clusters. Any two clusters that are closer than this minimal distance should be ...
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1answer
123 views

What is graph clustering?

There are several (family of) algorithms that can be used to cluster a set of $d$-dimensional points: for example, k-means, k-medoids, hierarchical clustering (agglomerative or divisive). What is ...
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1answer
109 views

How do we know the classification boundaries of the data?

Consider an image classification problem. Conceptually, we then have some high dimensional space where all the images can be represented as points, and having large enough labeled data set we can ...
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1answer
160 views

Is it normal that SOM clusters the instances with the “versicolor” class into multiple different BMUs?

I have trained (with different sizes, learning rates, and epochs) a SOM network to cluster the Iris dataset. The instances associated with the class setosa have ...