Classification
In order to classify high-dimensional data, machine learning algorithms that can deal with many dimensions (e.g. Support Vector Machines) can be used.
Clustering
If no training data is available, a cluster analysis can be performed. In both cases, the data must usually be normalized beforehand and sometimes the axes must also be transformed so that the base vectors have the same length in all dimensions and no dimension is preferred.
Individual Solutions
If an existing algorithm is suitable for the classification of the data, its parameters must be chosen correctly. However, if the data is too complex or if there are external dependencies that need to be integrated into the solution, a new algorithm must be developed.
Contact
256.systems can be contacted to help with classification algorithms, clustering, and other methods for extraction of information from large datasets.