An efficient and straightforward online quantization method for a data stream through remove-birth updating

TL;DR

This paper introduces a simple online vector quantization method that efficiently adapts to concept drift in data streams by removing low-probability units, reducing data volume and aiding drift detection.

Contribution

The proposed method is novel in its use of remove-birth updating for rapid adaptation to concept drift and minimal dead units in data streams.

Findings

Effective in handling concept drift

Reduces dead units in data streams

Metrics useful for drift detection

Abstract

The growth of network-connected devices has led to an exponential increase in data generation, creating significant challenges for efficient data analysis. This data is generated continuously, creating a dynamic flow known as a data stream. The characteristics of a data stream may change dynamically, and this change is known as concept drift. Consequently, a method for handling data streams must efficiently reduce their volume while dynamically adapting to these changing characteristics. This paper proposes a simple online vector quantization method for concept drift. The proposed method identifies and replaces units with low win probability through remove-birth updating, thus achieving a rapid adaptation to concept drift. Furthermore, the results of this study show that the proposed method can generate minimal dead units even in the presence of concept drift. This study also suggests that some metrics calculated from the proposed method will be helpful for drift detection.