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Inductive Logic Programming
This book constitutes the refereed proceedings of the 13th International Conference on Inductive Logic Programming, ILP 2003, held in Szeged, Hungary in September/October 2003. The 23 revised full papers presented were carefully reviewed and selected from 53 submissions. Among the topics addressed are multirelational data mining, complexity issues, theory revision, clustering, mathematical discovery, relational reinforcement learning, multirelational learning, inductive inference, description logics, grammar systems, and inductive learning.
Inductive Databases and Constraint-Based Data Mining
This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant pro...
Inductive Logic Programming
The Twelfth International Conference on Inductive Logic Programming was held in Sydney, Australia, July 9–11, 2002. The conference was colocated with two other events, the Nineteenth International Conference on Machine Learning (ICML2002) and the Fifteenth Annual Conference on Computational Learning Theory (COLT2002). Startedin1991,InductiveLogicProgrammingistheleadingannualforumfor researchers working in Inductive Logic Programming and Relational Learning. Continuing a series of international conferences devoted to Inductive Logic Programming and Relational Learning, ILP 2002 was the central event in 2002 for researchers interested in learning relational knowledge from examples. The Progr...
Progress in Artificial Intelligence
This book constitutes the refereed proceedings of the 12th Portuguese Conference on Artificial Intelligence, EPIA 2005, held in Covilhã, Portugal in December 2005 as nine integrated workshops. The 58 revised full papers presented were carefully reviewed and selected from a total of 167 submissions. In accordance with the nine constituting workshops, the papers are organized in topical sections on general artificial intelligence (GAIW 2005), affective computing (AC 2005), artificial life and evolutionary algorithms (ALEA 2005), building and applying ontologies for the semantic Web (BAOSW 2005), computational methods in bioinformatics (CMB 2005), extracting knowledge from databases and warehouses (EKDB&W 2005), intelligent robotics (IROBOT 2005), multi-agent systems: theory and applications (MASTA 2005), and text mining and applications (TEMA 2005).
Temporal Pattern Mining in Dynamic Environments
Many domains feature a dynamic characteristic like logistics, sports, or medicine and it would be useful to learn about frequent patterns, e.g., in what situations a traffic jam is likely to happen. Additionally, we are facing complex situations with many objects and relations that might change over time. The learning approach developed in this work identifies frequent temporal patterns out of qualitative, interval-based descriptions of dynamic scenes by extending the Apriori algorithm and combining ideas from relational as well as sequential association rule mining approaches. Temporal relations in patterns are represented by qualitative interval relations as they have been introduced by Allen and Freksa. The search for frequent patterns is a top-down approach starting by the most general (empty) pattern and performing specialization steps by applying an optimal refinement operator. In a second step, prediction rules are generated by splitting the identified patterns into precondition and consequence parts. The developed concepts are implemented in the MiTemP system and are evaluated on synthetic data and soccer matches of the RoboCup simulation league.
