tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 92

Data Mining and Knowledge Discovery Handbook, 2 Edition part 92. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 890 Saso DZeroski Mining one can search exhaustively or heuristically greedy search best-first search etc. . Just as for the single table case the space of patterns considered is typically lattice-structured and exploiting this structure is essential for achieving efficiency. The lattice structure is traversed by using refinement operators Shapiro 1983 which are more complicated in the relational case. In the propositional case a refinement operator may add a condition to a rule antecedent or an item to an item set. In the relational case a link to a new relation table can be introduced as well. Just as many Data Mining algorithms come from the field of machine learning many RDM algorithms come form the field of inductive logic programming Muggleton 1992 Lavrac and DZeroski 1994 . Situated at the intersection of machine learning and logic programming ILP has been concerned with finding patterns expressed as logic programs. Initially ILP focussed on automated program synthesis from examples formulated as a binary classification task. In recent years however the scope of ILP has broadened to cover the whole spectrum of Data Mining tasks classification regression clustering association analysis . The most common types of patterns have been extended to their relational versions relational classification rules relational regression trees relational association rules and so have the major Data Mining algorithms decision tree induction distance-based clustering and prediction etc. . Van Laer and De Raedt Van Laer and De Raedt 2001 DZeroski and Lavrac 2001 present a generic approach of upgrading single table Data Mining algorithms propositional learners to relational ones first-order learners . Note that it is not trivial to extend a single table Data Mining algorithm to a relational one. Extending the key notions to . defining distance measures for multi-relational data requires considerable insight and creativity. Efficiency concerns are also very important as it is .