Abstract
Data Stream mining presents unique challenges compared to traditional mining on a random sample drawn from a stationary statistical distribution. Data from real-world data streams are subject to concept drift due to changes that take place continuously in the underlying data generation mechanism. Concept drift complicates the process of mining data as models that are learnt need to be updated continuously to reflect recent changes in the data while retaining relevant information that has been learnt from the past. In this paper, we describe a Concept Based Decision Tree (CBDT) learner and compare it with the CVDFT algorithm, which uses a sliding time window. Our experimental results show that CBDT outperforms CVFDT in terms of both classification accuracy and memory consumption.
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Hoeglinger, S., Pears, R., Koh, Y.S. (2009). CBDT: A Concept Based Approach to Data Stream Mining. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_107
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DOI: https://doi.org/10.1007/978-3-642-01307-2_107
Publisher Name: Springer, Berlin, Heidelberg
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