Discussion of article "Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps"
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New article Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps has been published:
Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.
Kohonen Maps or Self-Organizing maps(SOM) or Self-Organizing Feature Map(SOFM). Is an unsupervised machine learning technique used to produce a low-dimensional(typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. For example; A dataset with p variables measured in n observations could be represented as clusters of observations with similar values for the variables. These clusters then could be visualized as a "two-dimensional map" such that observations in proximal clusters have more similar values than the observations in distal clusters, This can make high-dimensional data easier to visualize and analyze.
Kohonen maps were developed by a Finnish mathematician known as Teuvo Kohonen in the 1980s.
Author: Omega J Msigwa