US Patent Application 18031406. Data Pruning Tool and Related Aspects simplified abstract
Contents
Data Pruning Tool and Related Aspects
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
Leonard Rexberg of Hässelby (SE)
Data Pruning Tool and Related Aspects - A simplified explanation of the abstract
This abstract first appeared for US patent application 18031406 titled 'Data Pruning Tool and Related Aspects
Simplified Explanation
The patent application describes a method for determining regions of interest in a multi-dimensional data set.
- The method uses a self-organizing map (SOM) model to map the data set onto a surface mesh of neurons.
- Clusters of neurons are identified based on the assessed characteristic of the data.
- Ranges of boundary values for selection conditions are determined for each cluster.
- Regions of interest are determined by associating the boundary values with test case identifiers.
- The method can be used as a data pruning tool.
Original Abstract Submitted
A method and related aspects are disclosed for determining one or more regions of interest in a multi-dimensional data set comprising a plurality of parameter sets, each parameter set comprising a parameter set identifier, a plurality of dimensions of selection conditions for assessing a configurable physical entity, and an indication of an assessed characteristic of the configurable physical entity. The method comprises at least mapping, using a self-organising map, SOM, model which uses competitive group learning, the multi-dimensional data set onto an edge-connected surface mesh of neurons; identifying at least one cluster of neurons on the surface mesh based on a category of the assessed characteristic; identifying a set of ranges of boundary values for the selection conditions for each cluster, each range of boundary values comprising a maximum and a minimum weight value of the weights representing that selection condition of the neurons in that cluster; and determining one or more regions of interest which associate the boundary values of the selection conditions of each cluster with one or more test case identifiers for the test cases represented by the neurons in that cluster. The method may be implemented in some embodiments as a data pruning tool.
