Questions tagged [self-organizing-maps]

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

Filter by
Sorted by
Tagged with
0
votes
0answers
22 views

Self Organizing Maps - Mapping a single vs more layers

Suppose we train a Self Organizing Map (SOM) with two input layers, meaning we have the following situation: We have a vector $x=(x_1,...,x_n)\in\mathbb{R}^n$ which could represent biometric ...
0
votes
0answers
9 views

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 ...
0
votes
0answers
5 views

error in runing SOM

I am trying to run a SOM for my both qualitative data in five-levels (1-5) and quantitative data for 23 variables. unfortunately, I got this error SOM set.seed(222) g <- somgrid(xdim = 4, ...
0
votes
0answers
18 views

Should I care about stationarity when dealing with cyclical components?

I have downloaded time series data from the IMF-IFS website for 31 different countries. The time series are about GDP, constant prices, national currency (yearly); lending interest rate (monthly); ...
0
votes
1answer
35 views

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 ...
0
votes
2answers
329 views

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 ...
0
votes
1answer
141 views

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 ...
0
votes
1answer
138 views

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 ...
5
votes
0answers
340 views

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 ...
0
votes
1answer
38 views

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?
11
votes
1answer
99 views

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 ...
0
votes
1answer
98 views

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 ...
1
vote
1answer
80 views

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 ...
2
votes
1answer
423 views

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....
0
votes
1answer
32 views

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)?
0
votes
1answer
719 views

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, ...
0
votes
1answer
258 views

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 ...
0
votes
1answer
41 views

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-...
0
votes
2answers
57 views

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 ...
0
votes
3answers
559 views

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) ...
0
votes
1answer
95 views

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 ...
1
vote
0answers
184 views

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 ...
0
votes
1answer
194 views

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? Thanks
4
votes
1answer
2k views

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 ...
2
votes
1answer
704 views

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 ...
0
votes
1answer
85 views

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 ...
0
votes
1answer
210 views

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 ...
1
vote
1answer
41 views

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 ...
2
votes
1answer
257 views

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 ...
1
vote
1answer
201 views

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 ...
0
votes
1answer
238 views

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, ...
-1
votes
1answer
513 views

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 ...
3
votes
1answer
493 views

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 ...
4
votes
3answers
8k views

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 ...
1
vote
0answers
351 views

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 ...
1
vote
1answer
54 views

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 ...
1
vote
2answers
1k views

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 ...
2
votes
1answer
991 views

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 ...
3
votes
2answers
1k views

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 ...
1
vote
0answers
276 views

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 ...
2
votes
1answer
419 views

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 ...
2
votes
1answer
615 views

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?
1
vote
1answer
474 views

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 ...
0
votes
1answer
81 views

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. ...
5
votes
2answers
1k views

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 ...
4
votes
1answer
461 views

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 ...
4
votes
0answers
858 views

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 ...
1
vote
2answers
286 views

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 "...
34
votes
3answers
10k views

(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 ...
4
votes
0answers
367 views

Kohonen SOM for high (50-100) dimensions

Does a Kohonen-style SOM, using Euclidean distance, work as well as, better than, or worse than alternatives (K-means, etc) in high (50-100 or more) dimensional space? EDIT: I'm thinking particularly ...