Questions tagged [self-organizing-maps]

SOM is a kind of neural network used for clustering unlabeled data.

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Cluster Validation and Sample Size [closed]

I've recently been using the aweSOM package in R https://cran.r-project.org/web/packages/aweSOM/vignettes/aweSOM.html. The objective is to generate a SOM then impose partitive clustering on top, in ...
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How do I select the x- and y-dimension of a SOM?

I'm trying to use a SOM in R and I'm unsure if I am using some parameters correctly. I've seen that a typical rule of thumb is to set 'the dimensions' of the SOM to be: ...
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What the difference between clustering methods?

I am focusing on a thesis for introducing clustering methods. Chapter 3 includes hierarchical clustering, partitional clustering and density-based clustering. Meanwhile, chapter 4 is mainly on Self-...
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Self organized maps on mixed dataset (categoricals / numerics features )

i have a dataset of mixed variables and i want to apply self organized maps on it how can i extend som to mixed dataset? can i use the gower distance instead of euclidienne distance in order to ...
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Partitioning of a Self Organizing Map (SOM) in n clusters

I created the following 9x9 SOM (yellow = small distance between two neurons, red/black = large distance): I know that I can regard each neuron as a separate cluster and that neurons with small ...
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Is the grid in a self organizing map static?

I'm trying to write my own SOM in python, and after reading material from several sources (and watching video tutorials) I think I understand all the steps. There is however one issue that I want to ...
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What happens if the neighborhood radius in SOM set to zero?

In a project about the self-organizing map, in which I used miniSom library when I used 0 for the neighborhood radius (sigma) only one node was the winner (node 0,0). I would like to know if it is due ...
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How to inform the space and time complexity of K-means, SOM and Hierachical clustering

In the paper I am writing, one of the reviewers asked for an "a simple computational complexity analysis or time computational demands of their method" My question is : Can I simply report the ...
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Training a SOM in batch mode

I was reading the Somoclu parallel implementation of Self-Organizing Maps (SOMs) and they say that in order to make the algorithm parallelizable, a batch training mode has to be followed. The equation ...
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Differences between t-SNE and SOM

I have some high dimensional data and I want to reduce it to 2 dimensions for visualization. The goal is to color the points in this 2D space to see whether there is any clustering due to different ...
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Self-organizing map dimension

I just have a question on the chosen dimension of Self-organizing map. Typically, an SOM has a dimension of 2 or 3 but rarely larger than that. Is there a particular reason why this is the case?
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How to automatically cluster a U-Matrix?

After training a self-organising map, one can calculate the U-Matrix. There are some tools to manually visualize it and identify clusters, but I'm wondering if there is any algorithm to do this ...
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One-hot encoding for SOM

I have a question regarding how I should convert categorical data to numerical data. I'm using this kdd99cup intrusion detection dataset, which has a 41 attributes and class label is the type of ...
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What is the difference between these distances in self organized maps

I am building an anomaly model and am confused between these distances below. What is the difference between these distances in self organized maps. som.iris$distances dist(som.iris$codes[[1]]) As ...
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Why SOM is better than clustering technique(e.g. hierarchical)?

I am using SOM for dimension reduction and visualization purpose (to put the same observations together). I am using kohonen r-package for the same. https://cran....
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Self-Organizing maps : is a N*M grid the same as a M*N grid? (with M different from N)

Self-Organizing maps : will a N*M grid give the same results as a M*N grid? (with M different from N)?
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Should I standardize my data or not?

I am currently working on a dataset concerning the color magnitude of astronomical point sources. There are 9 covariates, each representing a specific color of a point source. I used k-means, ...
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normalization of time series for clustering via self-organizing maps

I have weekly average values aka time series. They are the same units (e.g. revenue in £s). I want to use self-organizing maps in its standard setting (using euclidean distances) to order/cluster ...
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Encode overlapped areas as inputs for Neural Networks (SOM)?

How am I supposed to represent area input variables to SOM, with the weights/distances to be based on how much they overlap? I'm trying to encode ranges of finite and positive integers. Like: 500-...
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Clustering with Self Organizing Maps including time, date and month as attributes

I am about to start up a project on pattern recognition in a highdimensional dataset holding information on transactional salesdata for a company. In that manner I have decided to use the method of ...
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Kohonen Self-Organizing Maps algorithm clarification. On each iteration, it goes trough all dataset or just a subsample?

If I understood the algorithm properly it should go like this: Randomly initialize SOM weights $\xi_1, ... , \xi_n$ in feature space. Randomly pick sample from training set v(k) (k as in step k) ...
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Quantify similarity between self-organizing maps (SOMs)

What would be a valid similarity measure to quantify the (dis)similarity between two different datasets processed using the same trained version of a self-organizing map (trained on the combination of ...
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Similar to Self Organization Maps, are all other clustering algorithms "self organizing"?

From my understanding of the terms: Self means: No supervision is required during training. Organizing means: To create a topographic ordered map by using unsupervised competitive learning and ...
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How to calculate explained variance of Self Organizing Map

Learning SOM recent days, but getting curious how does the explained variance of SOM is calculated. All the articles I have seen ignore this topic. Can anyone give some ideas?
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SOM grid size suggested by Vesanto

I am a bit confused on the size of the SOM grid size suggested by Vesanto. Here in this link, it says 5*sqrt(N) where N is mentioned as the dataset size. What is ...
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Can we use self organizing maps to build a reconstruction model such as autoencoder or generative adversarial network?

MLP and its variants can be used to build a reconstruction model such as conventional autoencoders including many variants and GAN, for example. I wonder if SOM can be a candidate to replace the ...
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Self-Organizing Maps clarity

I have spent sometime reading on self organizing maps. However i am still confused around a few areas. Broadly i understand its a visual technique to reduce high dimensional data - using artificial ...
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Lengths of principal components to determinate SOM grid

"Most applications of the SOM are based on regular arrays of nodes. Sometimes one uses rectangular arrays of nodes for simplicity. However, the hexagonal arrays are visually much more illustrative and ...
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Ideas to create better self-organized maps using textual data; issues dealing with highly sparse data

I have a term-document frequency matrix which is thus high-dimensional and very sparse. Whenever I generate SOMs in the "Kohonen" package in R I get one node dominating the others no matter which ...
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Empty nodes when creating SOM

I am trying to create a SOM map based on records with different discrete classifications (tags) like the example below ...
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Why SOM shows the best accuracy at 11x11?

I have started working on Human Action recognition using depth images. I found this article. In the experiment section, they have told that they have experimented SOM of different size and for the ...
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Dimensionality Reduction of Self Organising Maps

I've probably read any article on dimensionality reduction of Self Organising Maps but just couldn't fully comprehend this process. My understanding so far is: SOM are two-layer networks, whose ...
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Help in implementing self organizing map for quantizing time series based on a paper

Chapter titled, Self Organized Partitioning of Chaotic Attractors for Control in Lecture Notes in Computer Science in book: Artificial Neural Networks — ICANN 2001, pp.851-856 uses multiple self ...
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Kohonen SOM in R: How to give weights for certain variables in the BMU finding process?

I'm using the Kohonen package (see also self-organising-maps-for-customer-segmentation-using-r) for Self Organizing Maps (SOM), and I would like to know how to give weights for certain variables in ...
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Self organizing maps vs k-means, when the SOM has a lot of nodes

On Wikipedia it says: It has been shown that while self-organizing maps with a small number of nodes behave in a way that is similar to k-means, larger self-organizing maps rearrange data in a way ...
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SOM - which topological error and average distance are acceptable?

I have calaculated a SOM (with the kohonen package in R, 18x18 heaxagonal grid, 500 iterations, 92 variables, 1189 cases) and am currently trying to access it's usability. Calling a functions for ...
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Number of neighbors as a function of dimension

I apologize in advance for perhaps an imprecise formulation of the question. If I have a point in 1D, it has precisely 2 nearest neighbors independent of choices. In 2D, if I allow arbitrary ...
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SOM based on a not euclidean distance

Suppose one has trained a SOM on a certain number of data. Without explaining all the procedure, one can say that the SOM algorithm produces a certain number of prototypes and the new elements coming ...
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Self Organizing Map and input normalizing

I've been playing around with self organizing maps (SOM) recently. I tried to implement a simple example. You can see the training implementation function gist here and full contained SOM example ...
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How does one visualize the self-organizing map of $n$-dimensional data

I have a data set consisting from $7$-dimensional data points. I want to produce a self-organizing map for this data to see how my data is clustered. I have been reading some tutorials from the ...
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Cluster validation method for no cluster labels and differently sized clusters

I'm primarily a programmer and have little to no training in formal maths or statistics of any kind. I'm working on my dissertation (which foolishly is about clustering data), the process is ...
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Self Organizing Maps: How is the location computed and updated?

I have read other similar questions on here, but I am still unsure how SOM deals with the positions/locations of the neurons. Say that the input space is N-dimensional. I initalise some weights, and ...
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Are Self organizing maps networks a variant of Multi Layer Perceptron?

I'm learning Self Organizing Map (SOM) networks and I think I can say that SOM networks are a Multi Layer Perceptron (MLP) network. Is it correct to say that SOM is a variant of MLP?
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SOM (Kohonen) using the term document matrix [closed]

Language: R Package: kohonen Function: som I have a term document matrix (tdm) with 64 terms (row) and 1017 documents (columns). I want to use the self-organized-map to cluster the terms on 25 ...
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Is that correct about dimensionality reduction and clustering?

Could you please help me to understand it because I'm not sure if I got it correctly. Let's say I have a dataset, of persons, with 100 features, various characteristics like height, weight, age, etc. ...
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How do I compare multiple runs of K-means?

I have results of best centroids for multiple (10) runs of k-means. How do I compare these weights to check if they are close to each other or different? My goal is to check weather I get to the ...
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How do i compare two Self Organizing Maps?

I have results (weights) for multiple runs of self organizing map. I am trying to compare these results to check if my algorithm gets to the same solution from different random initial weights. I have ...
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What is the main differences between choosing hexagonal grid and rectangular grid for SOM?

While I'd expect people to answer this question by saying 'depends on the distribution of data', but what are the thumb rules for deciding which grid to use (either hexagonal or rectangular) for ...
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Dimensional reduction in Self-Organizing Maps: how to map a multidimensional Vector to a low-dimensional Grid

Good Day to everyone. I have spent quite some time now, introducing myself to neural networks. Therefore i am also looking into SOM's. Of course also on this site, as far as i have potentially "...
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(Why) Has Kohonen-style SOM fallen out of favor?

As far as I can tell, Kohonen-style SOMs had a peak back around 2005 and haven't seen as much favor recently. I haven't found any paper that says that SOMs have been subsumed by another method, or ...
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