Change Detection in Categorical Evolving Data Streams

Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution. To cope with these issues, we propose a new unsuper-vised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for cate-gorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a de-scriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learn-ing task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream