Knowledge Discovery in Databases

  title={Knowledge Discovery in Databases},
  author={Roland D{\"u}sing},
  • R. Düsing
  • Published 2000
  • Computer Science
  • Wirtschaftsinformatik
This is a manuscript of a textbook evolving from research and three years of teaching at the Hong Kong University of Science and Technology. The textbook gives an introduction into the fascinating eld of knowledge discovery in databases, sometimes called data mining. The manuscript is suited for beginners who can leave out the more advanced sections, as well as people who would like to do research in this area. In the manuscript emphasizes our own discovery techniques. Statistical and neural… 


Systems for Knowledge Discovery in Databases
A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems and is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench.
Data Mining: the search for knowledge in databases.
A survey of current data mining research, the main underlying ideas, such as inductive learning, and search strategies and knowledge representations used in data mine systems are presented, and the most important problems and their solutions are described.
A Probabilistic Query Language
A new query language for data and knowledge bases able to deal with weighted or uncertain base and derived information and reduces to a known query language in case that all information is deterministic is proposed.
Database Mining: A Performance Perspective
The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented and an algorithm for classification obtained by combining the basic rule discovery operations is given.
Discovery of Data Evolution Regularities in Large Databases
This paper describes an attribute-oriented induction technique for discovery of data evolution regularities in relational databases and shows that it substantially reduces the computational complexity of the knowledge discovery process.
An Overview of Database Mining Techniques
An architecture of the Knowledge Miner environment and its relation to the LDL++ deductive database technology is described and the notion of a meta query is shown and shows how this concept can be used to take advantage of the full power of a deductive data base system.
Automated learning of decision rules for text categorization
It is shown that machine-generated decision rules appear comparable to human performance, while using the identical rule-based representation, and compared with other machine-learning techniques.
The Management of Probabilistic Data
A data model that includes probabilities associated with the values of the attributes, and the notion of missing probabilities is introduced for partially specified probability distributions, offers a richer descriptive language allowing the database to more accurately reflect the uncertain real world.
Inductive Learning in Deductive Databases
Most current applications of inductive learning in databases take place in the context of a single extensional relation. The authors place inductive learning in the context of a set of relations
Fast algorithms for mining association rules
Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.