Aspect Oriented Concept Drift Detection in High Dimensional Data Streams
Sankara Prasanna Kumar M · International Journal of Advanced Trends in Computer Science and Engineering · 2020
The drift of the concept is the critical goal of data mining over data transmission, which often deotes the diversity between the pair of sequentially transmitted data tuples.The drift of the concept can be incremental, which increases gradually in the face of data transmission.The other dimension of the drift concept is sudden drift that manifests itself considerably between the pair of tuples of transactions transmitted in sequence.Contemporary Concept drift identification approaches are primarily intended to deal with incremental or sudden drift.In this document, aspect-oriented concept drift detection (AOCDD) is projected into high-dimension data streams.To report concept drift, the AOCDD represents the diversity of data projection for the aspects that are used to frame the record structure in the target data streams.The experiments carried out the reference data sets as flows, which show the importance and scalability of the AOCDD for the detection of drift.The performance advantage of the proposal is scaled by comparing the experimental results with another contemporary model in recent literature.