Question

K-means, self-organizing maps, hierarchical clustering are the example of​

Answer 1

The self-organizing map (SOM) has emerged as one of the popular choices for clustering data; however, when it comes to point density accuracy of codebooks or reliability and interpretability of the map, the SOM leaves much to be desired. In this paper, we compare the newly developed K-means hierarchical (KMH) clustering algorithm to the SOM. We also introduce a new initialization scheme for the K-means that improves codebook placement and, propose a novel visualization scheme that combines the principal component analysis (PCA) and minimal spanning tree (MST) in an arrangement that ensures reliability of the visualization unlike the SOM. A practical application of the algorithm is demonstrated on a challenging bioinformatics problem.

Answer 2

Answer:

Unsupervised learning

Explanation:

• This type of learning is not based on any specific type of the datasets.
• The data for these types of learning are used from the activity of the humans brain.
• With this type of learning the models are trained with the help of the untrained or unlabeled datasets.
• They just need to put any random data and then output will be checked.

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