Outlier detection in self-organizing maps and their quality estimation

Title:

Outlier detection in self-organizing maps and their quality estimation

Creator:

Stefanovič, Pavel and Kurasova, Olga

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:ecdf7c95-c673-4800-9889-34a7912cf848
uuid:ecdf7c95-c673-4800-9889-34a7912cf848
doi:10.14311/NNW.2018.28.006

Subject:

outlier detection and rejection, self-organizing map, and SOM quality estimation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In the paper, an algorithm that allows to detect and reject outliers in a self-organizing map (SOM) has been proposed. SOM is used for data clustering as well as dimensionality reduction and the results obtained are presented in a special graphical form. To detect outliers in SOM, a genetic algorithm-based travelling salesman approach has been applied. After outliers are detected and removed, the SOM quality has to be estimated. A measure has been proposed to evaluate the coincidence of data classes and clusters obtained in SOM. A larger value of the measure means that the distance between centers of different classes in SOM is longer and the clusters corresponding to the data classes separate better. With a view to illustrate the proposed algorithm, two datasets (numerical and textual) are used in this investigation.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2018 Volume:28 | Number:2

Harvested from:

CDK

Metadata only:

false