@article {966,
	title = {Survey on using constraints in data mining},
	journal = {Data Mining and Knowledge Discovery},
	volume = {31},
	number = {2},
	year = {2017},
	pages = {424{\textendash}464},
	abstract = {This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints in a data mining task requires specific definition and satisfaction tools during knowledge extraction. This survey proposes three groups of studies based on classification, clustering and pattern mining, whether the constraints are on the data, the models or the measures, respectively. We consider the distinctions between hard and soft constraint satisfaction, and between the knowledge extraction phases where constraints are considered. In addition to discussing how constraints can be used in data mining, we show how constraint-based languages can be used throughout the data mining process.},
	doi = {10.1007/s10618-016-0480-z},
	author = {Valerio Grossi and Andrea Romei and Franco Turini}
}
@conference {973,
	title = {The layered structure of company share networks},
	booktitle = {Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a framework for the analysis of corporate governance problems using network science and graph algorithms on ownership networks. In such networks, nodes model companies/shareholders and edges model shares owned. Inspired by the widespread pyramidal organization of corporate groups of companies, we model ownership networks as layered graphs, and exploit the layered structure to design feasible and efficient solutions to three key problems of corporate governance. The first one is the long-standing problem of computing direct and indirect ownership (integrated ownership problem). The other two problems are introduced here: computing direct and indirect dividends (dividend problem), and computing the group of companies controlled by a parent shareholder (corporate group problem). We conduct an extensive empirical analysis of the Italian ownership network, which, with its 3.9M nodes, is 30{\texttimes} the largest network studied so far.},
	doi = {10.1109/DSAA.2015.7344809},
	author = {Andrea Romei and Salvatore Ruggieri and Franco Turini}
}
@article {982,
	title = {A multidisciplinary survey on discrimination analysis},
	journal = {The Knowledge Engineering Review},
	volume = {29},
	number = {5},
	year = {2014},
	pages = {582{\textendash}638},
	abstract = {The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.},
	doi = {10.1017/S0269888913000039},
	author = {Andrea Romei and Salvatore Ruggieri}
}
@article {632,
	title = {Discrimination discovery in scientific project evaluation: A case study},
	journal = {Expert Systems with Applications},
	volume = {40},
	number = {15},
	year = {2013},
	pages = {6064{\textendash}6079},
	author = {Andrea Romei and Salvatore Ruggieri and Franco Turini}
}
@conference {GrossiRR08,
	title = {A Case Study in Sequential Pattern Mining for IT-Operational Risk},
	booktitle = {ECML/PKDD (1)},
	year = {2008},
	pages = {424-439},
	author = {Valerio Grossi and Andrea Romei and Salvatore Ruggieri}
}
@inbook {472,
	title = {Discovering Strategic Behaviour in Multi- Agent Scenarios by Ontology-Driven Mining},
	booktitle = {Advances in Robotics, Automation and Control},
	year = {2008},
	isbn = {978-953-7619-16-9},
	url = {http://www.intechopen.com/books/advances_in_robotics_automation_and_control/discovering_strategic_behaviors_in_multi-agent_scenarios_by_ontology-driven_mining},
	author = {Davide Bacciu and Andrea Bellandi and Barbara Furletti and Valerio Grossi and Andrea Romei}
}
@conference {469,
	title = {Ontological Support for Association Rule Mining},
	booktitle = {IASTED International Conference on Artificial Intelligence and Applications (AIA)},
	year = {2008},
	address = {Innsbruck, Austria },
	author = {Barbara Furletti and Andrea Bellandi and Valerio Grossi and Andrea Romei}
}
@conference {468,
	title = {Ontology-Driven Association Rule Extraction: A Case Study},
	booktitle = {International Workshop on Contexts and Ontologies: Representation and Reasoning},
	year = {2007},
	month = {2007},
	address = {Roskilde, Denmark},
	url = {http://ceur-ws.org/Vol-298/paper1.pdf},
	author = {Barbara Furletti and Andrea Bellandi and Valerio Grossi and Andrea Romei}
}
@conference {467,
	title = {PUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH },
	booktitle = { IADIS International Conference WWW/Internet 2007},
	year = {2007},
	month = {2007},
	isbn = {978-972-8924-44-7},
	url = {http://www.iadisportal.org/digital-library/mdownload/pushing-constraints-in-association-rule-mining-an-ontology-based-approach},
	author = {Barbara Furletti and Andrea Bellandi and Andrea Romei and Valerio Grossi}
}
