Abstract
Data mining and machine learning must confront the problem of pattern maintenance because data updating is a fundamental operation in data management. Most existing data-mining algorithms assume that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new data. While there are many efficient mining techniques for data additions to databases, in this paper, we propose a decremental algorithm for pattern discovery when data is being deleted from databases. We conduct extensive experiments for evaluating this approach, and illustrate that the proposed algorithm can well model and capture useful interactions within data when the data is decreasing.
This work is partially supported by large grants from the Australian Research Council (DP0449535 and DP0559536), a China NSFC major research program (60496321), and a China NSFC grant (60463003).
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhang, S., Wu, X., Zhang, J., Zhang, C. (2005). A Decremental Algorithm for Maintaining Frequent Itemsets in Dynamic Databases. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_30
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DOI: https://doi.org/10.1007/11546849_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
Online ISBN: 978-3-540-31732-6
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