Discovery of knowledge in electrical power systems
Intelligent Manufacturing Systems, Volume # 9 | Part# 1
Authors
Jedrzejec, Bartosz; Nawarecki, Edward
Digital Object Identifier (DOI)
10.3182/20081205-2-CL-4009.00009
Page Numbers:
40-45
Index Terms
data reduction,genetic algorithms,databases systems,data storage,data processing
Abstract
This paper concerns the presentation phase of a knowledge discovery process with use of association rules. The employment of the association rules model, the major technique of data mining, to knowledge discovering from the data of energy consumption is presented. The Predictive Model Markup Language (PMML) and XQuery language are involved to facilitate an environment for the systematic examination of complex mining models. A genetic programming approach to reducing complex association rule models is also introduced in the final part of the paper.
References
[1] Abiteboul, S., Buneman, P., and Suciu, D. (2000). Data
on the Web: from relations to semistructured data and
XML. Morgan Kaufmann Publishers Inc., San Francisco,
CA, USA.
[2] Agrawal, R. and Srikant, R. (1994). Fast algorithms for
mining association rules. In J.B. Bocca, M. Jarke, and
C. Zaniolo (eds.), Proc. 20th Int. Conf. Very Large Data
Bases, VLDB, 487-499. Morgan Kaufmann.
[3] Das, G., Lin, K.I., Mannila, H., Renganathan, G., and
Smyth, P. (1998). Rule discovery from time series. In
Knowledge Discovery and Data Mining, 16-22.
[4] Data Mining Group (1997-2007).
http://www.dmg.org/pmml-v3-2.html.
[5] Han, J., Fu, Y., Wang, W., Koperski, K., and Zaiane,
O. (1996). Dmql: A data mining query language for
relational databases.
[6] Hipp, J., Mangold, C., Guntzer, U., and Nakhaeizadeh, G.
(2002). Efficient rule retrieval and postponed restrict
operations for association rule mining. In Pacific-Asia
Conference on Knowledge Discovery and Data Mining,
52-65.
[7] Imielinski, T., Virmani, A., and Abdulghani, A. (1999).
Dmajor-application programming interface for database
mining. Data Min. Knowl. Discov., 3(4), 347-372.
[8] Koza, J.R. (1992). Genetic Programming: On the programming
of computers by means of natural selection.
Bradford Books, Cambridge, MA.
[9] Meo, R., Psaila, G., and Ceri, S. (1996). A new SQL-like
operator for mining association rules. In The VLDB
Journal, 122-133.
[10] Meo, R., Psaila, G., and Ceri, S. (1998). A tightly-coupled
architecture for data mining. In ICDE '98:
Proceedings of the Fourteenth International Conference
on Data Engineering, 316-323. IEEE Computer Society,
Washington, DC, USA.
[11] Michalewicz, Z. (1996). Genetic Algorithms + Data Structures
= Evolution Programs, 3rd ed. Artificial Intelligence.
Springer-Verlag, Berlin.
[12] Petry, F.E., Buckles, B.P., Sadasivan, T., and Kraft, D.H.
(1994). The use of genetic programming to build queries
for information retrieval. In International Conference on
Evolutionary Computation, 468-473.
[13] Smyth, P. and Goodman, R.M. (1992). An information
theoretic approach to rule induction from databases.
IEEE Transactions on Knowledge and Data Engineering,
4(4), 301-316.
[14] Swider, K. and Jedrzejec, B. (2005). A query-driven
exploration of association rule models in PMML. In
R. Tadeusiewicz, A. Ligza, and M. Szymkat (eds.), Proc.
5th Int. Conference Computer Methods and Systems,
409-414. Oprogramowanie Naukowo-Techniczne.
[15] Swider, K., Jedrzejec, B., and Wysocki, M. (2008).
A Query Driven Exploration of Multiple Association
Rules. In C. Cotta, S. Reich, R. Schaefer, and A. Ligeza
(eds.), Knowledge-Driven Computing, Knowledge Engineering
and Intelligent Computations Series: Studies in
Computational Intelligence, volume 102, 283-288.
[16] Tuzhilin, A. and Liu, B. (2002). Querying multiple sets of
discovered rules. In KDD '02: Proceedings of the eighth
ACM SIGKDD international conference on Knowledge
discovery and data mining, 52-60. ACM, New York, NY,
USA.
[17] World Wide Web Consortium (2007).
XQuery 1.0: An XML Query Language.
Http://www.w3.org/TR/xquery/.
