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Advances in Association Mining: Privacy, Synthetic Data, and Measures Reevaluation

This compilation of selected papers from ECML/PKDD 2007 explores significant developments in association mining techniques. Key topics include privacy-preserving methods for market basket data analysis, realistic synthetic data generation for testing association rule mining algorithms, and a reevaluation of measures pertinent to large database mining. These studies showcase innovative approaches to enhance data privacy, improve testing methodologies, and reassess evaluation metrics in association rule mining, ultimately contributing to more effective data analytics practices.

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Advances in Association Mining: Privacy, Synthetic Data, and Measures Reevaluation

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  1. ECML/PKDD 2007 Selected Papers – Association Mining Martin Ralbovský

  2. Long papers • Guo, Guo, Wu: Privacy Preserving Market Basket Data Analysis randomizing data procedures suitable for other measures then confidence and support

  3. Short papers • Cooper, Zito: Realistic Synthetic Data for Testing Association Rule Mining better generated data for association mining algorithms evaluation • Wu, Chen, Han: Association Mining in Large Databases: A Re-examination of Its Measures

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