Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from data, often based on underlying large data sets. A major aspect of Knowledge Discovery is data mining, i.e. applying data analysis and discovery algorithms that produce a particular enumeration of patterns (or models) over the data. Knowledge Discovery also includes the evaluation of patterns and identification of which add to knowledge. Information retrieval (IR) is concerned with gathering relevant information from unstructured and semantically fuzzy data in texts and other media, searching for information within documents and for metadata about documents, as well as searching relational databases and the Web. Automation of information retrieval enables the reduction of what has been called "information overload". Information retrieval can be combined with knowledge discovery to create software tools that empower users of decision support systems to better understand and use the knowledge underlying large data sets.
Ana Fred, Instituto de Telecomunicações and Instituto Superior Técnico (University of Lisbon), Portugal
Alejandro Carrasco Muñoz, University of Seville, Spain
María Isabel Hartillo, University of Seville, Spain
Nicola Leone, University of Calabria, ItalyXindong Wu, Mininglamp Software Systems, China and University of Louisiana at Lafayette, United StatesRudi Studer, Karlsruhe Institute of Technology, GermanyRita Cucchiara, University of Modena and Reggio Emilia, ItalyOscar Pastor, Universidad Politécnica de Valencia, Spain
Publications:
It is planned to publish a short list of revised and
extended versions of presented papers with
Springer in a CCIS Series book
Proceedings will be submitted for evaluation for indexing by: