  Data stream poses additional challenges to statistical classification tasks
because distributions of the training and target samples may differ as time
passes. Such distribution change in streaming data is called concept drift.
Numerous histogram-based distribution change detection methods have been
proposed to detect drift. Most histograms are developed on grid-based or
tree-based space partitioning algorithms which makes the space partitions
arbitrary, unexplainable, and may cause drift blind-spots. There is a need to
improve the drift detection accuracy for histogram-based methods with the
unsupervised setting. To address this problem, we propose a cluster-based
histogram, called equal intensity k-means space partitioning (EI-kMeans). In
addition, a heuristic method to improve the sensitivity of drift detection is
introduced. The fundamental idea of improving the sensitivity is to minimize
the risk of creating partitions in distribution offset regions. Pearson's
chi-square test is used as the statistical hypothesis test so that the test
statistics remain independent of the sample distribution. The number of bins
and their shapes, which strongly influence the ability to detect drift, are
determined dynamically from the sample based on an asymptotic constraint in the
chi-square test. Accordingly, three algorithms are developed to implement
concept drift detection, including a greedy centroids initialization algorithm,
a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift
adaptation, we recommend retraining the learner if a drift is detected. The
results of experiments on synthetic and real-world datasets demonstrate the
advantages of EI-kMeans and show its efficacy in detecting concept drift.
