Abstract
This paper considers a rough set approach for the problem of finding minimal jumping emerging patterns (JEPs) in classified transactional datasets. The discovery is transformed into a series of transaction-wise local reduct computations. In order to decrease average subproblem dimensionality, we introduce local projection of a database. The novel algorithm is compared to the table condensation method and JEP-Producer for sparse and dense, originally relational data. For a more complete picture, in our experiments, different implementations of basic structures are considered.
The research has been partially supported by grant No 3 T11C 002 29 received from Polish Ministry of Education and Science.
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Terlecki, P., Walczak, K. (2008). Local Projection in Jumping Emerging Patterns Discovery in Transaction Databases. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_69
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DOI: https://doi.org/10.1007/978-3-540-68125-0_69
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