A Clustering-based Framework for Classifying Data Streams

TL;DR

This paper introduces a clustering-based framework for classifying non-stationary data streams that does not require initial labels, effectively handling concept drift and class overlap through density-based clustering and active learning strategies.

Contribution

It presents a novel, label-free clustering framework with dynamic thresholds and sub-cluster analysis for improved data stream classification.

Findings

Outperforms existing methods in accuracy and robustness.

Effectively detects and adapts to concept drift.

Handles class overlap with sub-cluster analysis.

Abstract

The non-stationary nature of data streams strongly challenges traditional machine learning techniques. Although some solutions have been proposed to extend traditional machine learning techniques for handling data streams, these approaches either require an initial label set or rely on specialized design parameters. The overlap among classes and the labeling of data streams constitute other major challenges for classifying data streams. In this paper, we proposed a clustering-based data stream classification framework to handle non-stationary data streams without utilizing an initial label set. A density-based stream clustering procedure is used to capture novel concepts with a dynamic threshold and an effective active label querying strategy is introduced to continuously learn the new concepts from the data streams. The sub-cluster structure of each cluster is explored to handle the overlap among classes. Experimental results and quantitative comparison studies reveal that the proposed method provides statistically better or comparable performance than the existing methods.