|
ABSTRACT
Data mining algorithms are now able to efficiently deal with huge amount of data. Various kinds of patterns may be discovered and may have some great impact on the general development of knowledge. In many domains, end users may want to have their data mined by data mining tools in order to extract patterns that could impact their business. Nevertheless, those users are often overwhelmed by the large quantity of patterns extracted in such a situation. Moreover, some privacy issues, or some commercial one may lead the users not to be able to mine the data by themselves. Thus, the users may not have the possibility to perform many experiments integrating various constraints in order to focus on specific patterns they would like to extract. Post processing of patterns may be an answer to that drawback. Thus, in this paper we present a framework that could allow end users to manage collections of patterns. We propose to use an efficient data structure on which some algebraic operators may be used in order to retrieve or access patterns in pattern bases.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
|
| |
2
|
|
| |
3
|
J. F. Boulicaut and C. Masson. Data mining query languages. In The Data Mining and Knowledge Discovery Handbook, pages 715--727. Springer, 2005.
|
| |
4
|
B. Catania and A. Maddalena. Pattern Management: Practice and Challenges, pages 280--317. Processing and Managing Complex Data for Decision Support. Idea Group Publishing, 2006.
|
| |
5
|
Barbara Catania , Anna Maddalena , Maurizio Mazza , Elisa Bertino , Stefano Rizzi, A framework for data mining pattern management, Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, p.87-98, September 20-24, 2004, Pisa, Italy
|
| |
6
|
C. T. Diop, A. Giacometti, D. Laurent, and N. Spyratos. Computation of mining queries: An algebraic approach. In Constraint-Based Mining and Inductive Databases, volume 3848 of LNCS, pages 102--126, 2005.
|
| |
7
|
R. L. Grossman, S. Bailey, A. Ramu, B. Malhi, P. Hallstrom, I. Pulleyn, and X. Qin. The management and mining of multiple predictive models using the predictive model markup language (PMML). In Information and Software Technology, volume 41, pages 589--595, 1999.
|
 |
8
|
|
| |
9
|
T. Mielikäinen. An automata approach to pattern collections. In KDID, volume 3377 of LNCS, pages 130--149, 2004.
|
| |
10
|
Panda. Patterns for next-generation database systems (2001-2004). FET/IST-2001-33058.
|
| |
11
|
K. Parsaye. From datamagement to pattern management. DM Rev. Mag., 1999.
|
 |
12
|
|
| |
13
|
|
| |
14
|
A. Soulet and B. Crémilleux. An efficient framework for mining flexible constraints. In PAKDD, volume 3518 of LNCS, pages 661--671, 2005.
|
 |
15
|
|
| |
16
|
R. Wille. Concept lattices and conceptual knowledge systems. Comp. math. applied, 23(6--9):493--515, 1992.
|
| |
17
|
R. Wille. Formal Concept Analysis: Mathematical Foundations. Springer, 1999.
|
| |
18
|
M. J. Zaki, N. Parimi, N. De, F. Gao, B. Phoophakdee, J. Urban, V. Chaoji, M. A. Hasan, and S. Salem. Towards generic pattern mining. In Proc. of the Conference on Formal Concept Analysis, volume 3403 of LNCS, pages 1--20, 2005.
|
|