106M1081-01 |BENTLY | Data output module

Description

106M1081-01 |BENTLY | Data output module

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This software also provides Self Organizing Map (SOM) clustering, 106M1081-01 is developed by T The model proposed by Konohen in 1980 belongs to unsupervised learning neural network clustering, similar to K-means. Before using the SOM clustering algorithm, the number of desired classes must also be estimated first. Then, a random vector is established for each part, and a gene is randomly selected. The distance between this vector and the expression vector is calculated using the selected distance rectangle matrix to determine the closest reference vector to the gene.
106M1081-01 then adjusts this reference vector to be closer to the expression vector, and other reference vectors are also adjusted accordingly. This process is constantly iterated, with a decrease in the amount of adjustment for the reference vector, but the rigor of similarity level continues to increase. Finally, the reference vector converges to a fixed value, and the gene is subsequently divided into several parts.

After clustering is completed, 106M1081-01 saves the data in a text file (. txt), with the output format shown in Figure 3. This software provides a distance matrix that can be generated during the clustering process and is also saved in a text file. This software can also output a clustering tree diagram (hierarchical chart). The following are the calculation results of the system clustering method and K-means clustering method:

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