Contents
Why is SOM better than K means?
K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. From the performance point of view, the K-means algorithm performs better than SOM if the number of clusters increases. K-means is more sensitive to the noise present in the dataset compared to SOM.
What is the difference between vector quantization and SOM?
Self organizing maps are more suited for clustering(dimension reduction) rather than classification. But SOM’s are used in Linear vector quantization for fine tuning. But LVQ is a supervised leaning method. So to use SOM’s in LVQ, LVQ should be provided with a labelled training data set.
How are self-organizing maps similar to k-means?
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 that is fundamentally topological in character. What does that means exactly? I understood the first part: A SOM with small nodes behaves like a k-means algorithm.
What’s the difference between SOM and self organizing maps?
Whereas in SOM (Self Organizing Maps), the number of neurons of the output layer has a close relationship with the class number in the input stack. In this, the clusters are formed geometrically. From the performance point of view, the K-means algorithm performs better than SOM if the number of clusters increases.
At an intuitive level, both k-means and SOM are moving nodes towards denser areas of your space. With k-means, the nodes move freely, with no direct relationship to each other. And a node that is responsible for zero or one data points is degenerate and the k-means algorithm must avoid this situation.
What’s the difference between k-means and clustering?
K-means is a greedy algorithm and is hard to attain the global optimum clustering results. In K-means the nodes (centroids) are independent of each other, clusters are formed through centroid (nodes) and cluster size.