Overviews on rule quality.- Choosing the Right Lens: Finding What is Interesting in Data Mining.- A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study.- Association Rule Interestingness Measures: Experimental and Theoretical Studies.- On the Discovery of Exception Rules: A Survey.- From data to rule quality.- Measuring and Modelling Data Quality for Quality-Awareness in Data Mining.- Quality and Complexity Measures for Data Linkage and Deduplication.- Statistical Methodologies for Mining Potentially Interesting Contrast Sets.- Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality.- Rule quality and validation.- A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link.- Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules.- Association Rule Interestingness: Measure and Statistical Validation.- Comparing Classification Results between N-ary and Binary Problems.