Questions tagged [k-means]
K-means algorithm groups given set of points (or vectors) into clusters, finding the groups of points that are closer together (by Euclidean distance).
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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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What are the benefits of using spectral k-means over simple k-means?
I have understood why k-means can get stuck in local minima.
Now, I am curious to know how the spectral k-means helps to avoid this local minima problem.
According to this paper A tutorial on Spectral,...
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How to group multi-dimensional audio, video, and numerical data based on relatedness?
I have a data set that includes image arrays, point clouds, audio waveforms, and plain numerical data. I want to use unsupervised learning to group the data based on relatedness. So, if the audio and ...
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How to use K-means clustering to visualise learnt features of a CNN model?
Recently, I was going through the paper Intriguing Properties of Contrastive Losses. In the paper (section 3.2), the authors try to determine how well the SimCLR framework has allowed the ResNet50 ...
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What is the borderline between unsupervised learning and regular algorithms?
Unsupervised learning using neural networks is clearly machine learning since it is utilising neural nets.
However, some algorithms, k-means clustering, for example, are considered unsupervised ...
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Why does k-means have more bias than spectral clustering and GMM?
I ran into a 2019-Entrance Exam question as follows:
The answer mentioned is (4), but some search on google showed me maybe (1) and (2) is equal to (4). Why would k-means be the algorithm with the ...
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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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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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Can I do state space quantization using a KMeans-like algorithm instead of range buckets?
Are there any reference papers where it is used a KMeans-like algorithm in state space quantization in Reinforcement Learning instead of range buckets?
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How can I classify instances into two categories and then into sub-categories, when the number of features is high?
I'm working with a problem where I have a lot of variables for different cases of different users. Depending on the values of the different variables of a concrete user in a concrete case, the ...
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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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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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Is there a machine learning algorithm to find similar sales patterns?
I have a dataset as follows
(and the table extends to include an extra 146 columns for companies 4-149)
Is there an algorithm I could use effectively to find similar patterns in sales from the other ...
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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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How to refine K-means clustering on a data set?
I'm working with a data set where the data is stored in a string such as AxByCyA where A, B ...
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