Abstract
An interestingness measure estimates the degree of interestingness of a discovered pattern and has been actively studied in the past two decades. Several pitfalls should be avoided in the study such as a use of many parameters and a lack of systematic evaluation in the presence of noise. Compression-based measures have advantages in this respect as they are typically parameter-free and robust to noise. In this paper, we present J-measure and a measure based on an extension of the Minimum Description Length Principle (MDLP) as compression-based measures for mining interesting rules.
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Suzuki, E. (2009). Compression-Based Measures for Mining Interesting Rules. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_75
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DOI: https://doi.org/10.1007/978-3-642-02568-6_75
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