N. Cercone – författare

In recent years AI has been experiencing a deep internal debate on the appropriateness of the symbolic-based paradigm and all of its consequences. While various symbolic representation schemes, as well as their integration, have been proposed, their limitations have continuously pushed researchers for improved versions or entirely new ones. New viewpoints such as the complex dynamic-based approach with neural nets can be regarded simply as new problem solving techniques with specific properties.Under this perspective, what seems to be important is the ability to combine heterogeneous representation and problem-solving techniques. Research on heterogeneous, intelligent systems goes hand in hand with research on specific problem solving methods and paradigms, therefore representing their conceptual and practical glueing element. The papers contained in this proceedings are just one instance of such awareness activity in the international scientific community.

An edited collection of research on the most recent developments in rough set theory and data mining, the chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases. This text should be a useful reference work for rough set researchers, database designers and developers, and for researchers new to the areas of data mining and rough sets.

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases. The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others. Rough Sets and Data Mining: Analysis of Imprecise Data will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases. The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others. Rough Sets and Data Mining: Analysis of Imprecise Data will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.