Abstract
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 deductive rules, and data mining algorithms are constructed by means of iterative user-defined aggregates. Iterative user-defined aggregates have a fixed scheme that allows the modularization of data mining algorithms, thus providing a way to exploit domain knowledge in the right point. As a case study, the paper presents user-defined aggregates for specifying a version of the apriori algorithm. Some performance analyses and comparisons are discussed in order to show the effectiveness of the approach.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
R. Agrawal, S. Sarawagi, and S. Thomas. Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications. Data Mining and Knowledge Discovery, 4(3):89–125, 2000.
P. Alcamo, F. Domenichini, and F. Turini. An XML Based Environment in Support of the Overall KDD Process. In Procs. of the 4th International Conference on Flexible Query Answering Systems (FQAS2000), Advances in Soft Computing, pages 413–424, 2000.
S. Chaudhuri and K. Shim. Optimization of Queries with User-Defined Predicates. ACM Transactions on Database Systems, 24(2):177–228, 1999.
D. Chimenti, R. Gamboa, and R. Krishnamurthy. Towards an Open Architecture for LDL. In Proc. 15th Int. Conf. on Very Large Data Bases (VLDB89), pages 195–204, 1989.
F. Giannotti and G. Manco. Querying Inductive Databases via Logic-Based User-Defined Aggregates. In Proc. 3rd European Conference on Principles and Practices of Knowledge Discovery in Databases, number 1704 in Lecture Notes on Artificial Intelligence, pages 125–135, September 1999.
F. Giannotti and G. Manco. Declarative Knowledge Extraction with Iterative User-Defined Aggregates. In Procs. 4th International Conference on Flexible Query Answering Systems (FQAS2000), Advances in Soft Computing, pages 445–454, 2000.
F. Giannotti and G. Manco. Making Knowledge Extraction and Reasoning Closer. In Proc. 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, number 1805 in Lecture Notes in Computer Science, April 2000.
F. Giannotti, G. Manco, M. Nanni, and D. Pedreschi. Nondeterministic, Nonmonotonic Logic Databases. IEEE Trans. on Knowledge and Data Engineering. To appear.
J. Han, Y. Fu, K. Koperski, W. Wang, and O. Zaiane. DMQL: A Data Mining Query Language for Relational Databases. In SIGMOD’96 Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’96), 1996.
T. Imielinski and A. Virmani. MSQL: A Query Language for Database Mining. Data Mining and Knowledge Discovery, 3(4):373–408, 1999.
G. Manco. Foundations of a Logic-Based Framework for Intelligent Data Analysis. PhD thesis, Department of Computer Science, University of Pisa, April 2001.
H. Mannila. Inductive databases and condensed representations for data mining. In International Logic Programming Symposium, pages 21–30, 1997.
R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory Mining and Pruning Optimizations of Constrained Associations Rules. In Proc. ACM Conf. on Management of Data (SIGMOD98), June 1998.
S. Ceri R. Meo, G. Psaila. A New SQL-Like Operator for Mining Association Rules. In Proc. 22th Int. Conf. on Very Large Data Bases (VLDB96), pages 122–133, 1996.
R. Srikant. Fast Algorithms for Mining Association Rules and Sequential Patterns. PhD thesis, University of Wisconsin-Madison, 1996.
C. Zaniolo, N. Arni, and K. Ong. Negation and Aggregates in Recursive Rules: The LDL ++ Approach. In Proc. 3rd Int. Conf. on Deductive and Object-Oriented Databases (DOOD93), volume 760 of Lecture Notes in Computer Science, 1993.
C. Zaniolo and H. Wang. Logic-Based User-Defined Aggregates for the Next Generation of Database Systems. In The Logic Programming Paradigm: Current Trends and Future Directions. Springer-Verlag, Berlin, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Giannotti, F., Manco, G., Turini, F. (2001). Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study. In: De Raedt, L., Siebes, A. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science(), vol 2168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44794-6_11
Download citation
DOI: https://doi.org/10.1007/3-540-44794-6_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42534-2
Online ISBN: 978-3-540-44794-8
eBook Packages: Springer Book Archive