A Study on Interactive Proteomic Data Clustering: A Comparison between Self-Organizing Map and Neural Gas | Chapter 1 | Research and Applications Towards Mathematics and Computer Science Vol. 7

The interconnected system algorithms Self-Organizing Map (SOM) and Affecting animate nerve organs Gas (NG) use unsupervised competing learning. These methods have the fault-finding virtue of maintaining the topological structure of the data, that means that data that are enclose the input distribution are plan to neighboring positions in the network or output. This characteristic form them intriguing to search in terms of dossier clustering. A crucial characteristic resolving vast amounts of data manually maybe challenging and time-consuming. Suitable way, technologies for analyzing and visualizing large multidimensional data sets are necessary.  We introduce a order for comparing and visualizing the SOM and NG in this branch. We describe these algorithms first, and then we create a pictorial comparison betwixt them. The protein mass spectrometry dossier clustering is then elucidated using these imagination approaches.

Author(s) Details:

Terje Solsvik Kristensen,
Department of Informatics, Western Norway University of Applied Sciences, Bergen, Norway and BIC AS, Norway.

Please see the link here: https://stm.bookpi.org/RATMCS-V7/article/view/12831