Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Communications of the ACM
Communications of the ACM
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Automating the analysis and cataloging of sky surveys
Advances in knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Statistical Themes and Lessons for Data Mining
Data Mining and Knowledge Discovery
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Knowledge Discovery in Databases: An Overview
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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Data Mining and Knowledge Discovery in Databases (KDD) promise to play an important role in the way people interact with databases, especially decision support databases where analysis and exploration operations are essential. Inductive logic programming can potentially play some key roles in KDD. We define the basic notions in data mining and KDD, define the goals, present motivation, and give a high-level definition of the KDD Process and how it relates to Data Mining. We then focus on data mining methods. Basic coverage of a sampling of methods will be provided to illustrate the methods and how they are used. We cover two case studies of successful applications in science data analysis, one of which is the classification of cataloging of a major astronomy sky survey covering 2 billion objects in the northern sky. The system can outperform human as well as classical computational analysis tools in astronomy on the task of recognizing faint stars and galaxies. We conclude with a listing of research challenges and we outline the areas where ILP could play some important roles in KDD.