Abstract
We study the tree induction over a stream of perennial objects. The perennial objects are dynamic in nature and cannot be forgotten. The objects come from a multi-table stream, e.g., streams of Customer and Transaction. As the Transactions arrive, the perennial Customers’ profiles grow and accumulate over time. To perform tree induction, we propose a tree induction algorithm that can handle perennial objects. The algorithm also encompasses a method that identifies and adapts to the concept drift in the stream. We have also incorporated a conventional classifier (kNN) at the leaves to further improve the classification accuracy of our algorithm. We have evaluated our method on a synthetic dataset and the PKDD Challenge 1999 dataset.
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Siddiqui, Z.F., Spiliopoulou, M. (2010). Tree Induction over Perennial Objects. In: Gertz, M., Ludäscher, B. (eds) Scientific and Statistical Database Management. SSDBM 2010. Lecture Notes in Computer Science, vol 6187. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13818-8_43
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DOI: https://doi.org/10.1007/978-3-642-13818-8_43
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