Clustering data in an uncertain environment using an artificial immune system

Clustering of data in an uncertain environment can result into different partitions of the data at different points in time. Therefore, the initial formed clusters of non-stationary data can adapt over time which means that feature vectors associated with different clusters can follow different migration types to and from other clusters. This paper investigates different data migration types and proposes a technique to generate artificial non-stationary data which follows different migration types. Furthermore, the paper proposes clustering performance measures which are more applicable to measure the clustering quality in a non-stationary environment compared to the clustering performance measures for stationary environments. The proposed clustering performance measures in this paper are then used to compare the clustering results of three network based artificial immune models, since the adaptability and self-organising behaviour of the natural immune system inspired the modelling of network based artificial immune models for clustering of non-stationary data.

Research highlights
► Different data migration types exist in a non-stationary environment.
► Some artificial immune models do not scale in a non-stationary environment.
► Some artificial immune models tend to overfit data in a non-stationary environment.
► The severity of change in data influences the clustering performance of a model.