Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Towards an open architecture for LDL
VLDB '89 Proceedings of the 15th international conference on Very large data bases
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Advanced database systems
Inductive databases and condensed representations for data mining (extended abstract)
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization techniques for queries with expensive methods
ACM Transactions on Database Systems (TODS)
Optimization of queries with user-defined predicates
ACM Transactions on Database Systems (TODS)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Semantics and expressive power of nondeterministic constructs in deductive databases
Journal of Computer and System Sciences
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Nondeterministic, Nonmonotonic Logic Databases
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Querying Inductive Databases: A Case Study on the MINE RULE Operator
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Querying Inductive Databases via Logic-Based User-Defined Aggregates
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
On Efficiently Implementing SchemaSQL on an SQL Database System
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Using SQL to Build New Aggregates and Extenders for Object- Relational Systems
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
The 3W Model and Algebra for Unified Data Mining
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Data Mining as Constraint Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Fast algorithms for mining association rules and sequential patterns
Fast algorithms for mining association rules and sequential patterns
Software—Practice & Experience
IQL: a proposal for an inductive query language
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
An inductive database system based on virtual mining views
Data Mining and Knowledge Discovery
A relational query primitive for constraint-based pattern mining
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
| Hi-index | 0.00 |
We present a way of exploiting domain knowledge in the design and implementation of data mining algorithms, with special attention to frequent patterns discovery, within a deductive framework. In our framework, domain knowledge is represented by way of deductive rules, and data mining algorithms are specified by means of iterative user-defined aggregates and implemented by means of user-defined predicates. This choice allows us to exploit the full expressive power of deductive rules without loosing in performance. Iterative user-defined aggregates have a fixed scheme, in which user-defined predicates are to be added. This feature allows the modularization of data mining algorithms, thus providing a way to integrate the proper domain knowledge exploitation in the right point. As a case study, the paper presents how user-defined aggregates can be exploited to specify and implement a version of the a priori algorithm. Some performance analyzes and comparisons are discussed in order to show the effectiveness of the approach.