Skip to main content

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).

Filter by
Sorted by
Tagged with
0 votes
1 answer

What does "Clustering features based on their values across objects rather than clustering objects ($X^T$ rather than$ X$)" mean?

In an ML book, it is said that: "Similarly, clustering is a popular choice for feature selection in classification-clustering features based on their values across objects rather than clustering ...
DSPinfinity's user avatar
0 votes
0 answers

Using nearest neighbor in RANSAC

I found many resources online talking about nearest neighbor concept in RANSAC. For example, figure 2 of this paper, this article and this repo talk about nearest neighbor in the context of RANSAC. ...
RajS's user avatar
  • 101
0 votes
0 answers

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
2 votes
0 answers

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,...
Amartya's user avatar
  • 121
0 votes
0 answers

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 ...
DragonflyRobotics's user avatar
1 vote
0 answers

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 ...
VEDANT JOSHI's user avatar
-1 votes
1 answer

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 ...
Dan D.'s user avatar
  • 1,283
0 votes
2 answers

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 ...
Lisa Berry's user avatar
2 votes
0 answers

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
2 votes
0 answers

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

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
1 answer

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?
ddaedalus's user avatar
  • 919
2 votes
0 answers

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 ...
notarealgreal's user avatar
1 vote
1 answer

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 ...
user9007131's user avatar
3 votes
2 answers

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 ...
h22's user avatar
  • 145
6 votes
2 answers

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 ...
Anonymous's user avatar
  • 163
3 votes
1 answer

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 ...
nbro's user avatar
  • 40.6k
1 vote
1 answer

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 ...
Jessica Chambers's user avatar