Abstract
Database classification is a data preprocessing technique for multi-database mining. To reduce search costs in the data from all databases, we need to identify those databases which are most likely relevant to a data mining application. Based on the related research, the algorithm GreedyClass and BestClassification [7]are improved in order to optimize the time complexity of algorithm and to obtainthe best classification from m given databases. Theoretical analysis and experimental results show the efficiency of the proposed algorithm.
The work was supported by the natural science fund from Anhui Education Department (serial number: KJ2008B122). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
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Li, H., Hu, X., Zhang, Y. (2009). An Improved Database Classification Algorithm for Multi-database Mining. In: Deng, X., Hopcroft, J.E., Xue, J. (eds) Frontiers in Algorithmics. FAW 2009. Lecture Notes in Computer Science, vol 5598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02270-8_35
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DOI: https://doi.org/10.1007/978-3-642-02270-8_35
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