Given a set of objects and a pairwise similarity measure between them, the goal of correlation clustering is to partition the objects in a set of clusters to maximize the similarity of the objects within the same cluster and minimize the similarity of the objects in different clusters. In most of the variants of correlation clustering, the number of clusters is not a given parameter; instead, the optimal number of clusters is automatically determined. Correlation clustering is perhaps the most natural formulation of clustering: as it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and, particularly, makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain. Despite its simplicity, generality, and wide applicability, correlation clustering has so far received much more attention from an algorithmic-theory perspective than from the data-mining community. The goal of this lecture is to show how correlation clustering can be a powerful addition to the toolkit of a data-mining researcher and practitioner, and to encourage further research in the area.
Given a set of objects and a pairwise similarity measure between them, the goal of correlation clustering is to partition the objects in a set of clusters to maximize the similarity of the objects within the same cluster and minimize the similarity of the objects in different clusters. In most of the variants of correlation clustering, the number of clusters is not a given parameter; instead, the optimal number of clusters is automatically determined. Correlation clustering is perhaps the most natural formulation of clustering: as it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and, particularly, makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain. Despite its simplicity, generality, and wide applicability, correlation clustering has so far received much more attention from an algorithmic-theory perspective than from the data-mining community. The goal of this lecture is to show how correlation clustering can be a powerful addition to the toolkit of a data-mining researcher and practitioner, and to encourage further research in the area.
Given a set of objects and a pairwise similarity measure between them, the goal of correlation clustering is to partition the objects in a set of clusters to maximize the similarity of the objects within the same cluster and minimize the similarity of the objects in different clusters. In most of the variants of correlation clustering, the number of clusters is not a given parameter; instead, the optimal number of clusters is automatically determined. Correlation clustering is perhaps the most natural formulation of clustering: as it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and, particularly, makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain. Despite its simplicity, generality, and wide applicability, correlation clustering has so far received much more attention from an algorithmic-theory perspective than from the data-mining community. The goal of this lecture is to show how correlation clustering can be a powerful addition to the toolkit of a data-mining researcher and practitioner, and to encourage further research in the area.
Derived from the renowned multi-volume International Encyclopaedia of Laws, this practical analysis of competition law and its interpretation in the Italy covers every aspect of the subject – the various forms of restrictive agreements and abuse of dominance prohibited by law and the rules on merger control; tests of illegality; filing obligations; administrative investigation and enforcement procedures; civil remedies and criminal penalties; and raising challenges to administrative decisions. Lawyers who handle transnational commercial transactions will appreciate the explanation of fundamental differences in procedure from one legal system to another, as well as the international aspects of competition law. Throughout the book, the treatment emphasizes enforcement, with relevant cases analysed where appropriate. An informative introductory chapter provides detailed information on the economic, legal, and historical background, including national and international sources, scope of application, an overview of substantive provisions and main notions, and a comprehensive description of the enforcement system including private enforcement. The book proceeds to a detailed analysis of substantive prohibitions, including cartels and other horizontal agreements, vertical restraints, the various types of abusive conduct by the dominant firms and the appraisal of concentrations, and then goes on to the administrative enforcement of competition law, with a focus on the antitrust authorities’ powers of investigation and the right of defence of suspected companies. This part also covers voluntary merger notifications and clearance decisions, as well as a description of the judicial review of administrative decisions. Its succinct yet scholarly nature, as well as the practical quality of the information it provides, make this book a valuable time-saving tool for business and legal professionals alike. Lawyers representing parties with interests in the Italy will welcome this very useful guide, and academics and researchers will appreciate its value in the study of international and comparative competition law.
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