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2000 | nr 850 | 159--173
Tytuł artykułu

Post-Processing of Association Rules

Warianty tytułu
Języki publikacji
EN
Abstrakty
W artykule przedstawiono badania mające na celu optymalizację algorytmów wiedzy. Zaprezentowano również dostępną literaturę poświęconą tematowi pozyskiwania wiedzy z baz danych. (MP)
EN
In this paper, we situate and motivate the need for a post-processing phase to the association rule mining algorithm when plugged into the knowledge discovery in databases process. Major research effort has already been devoted to optimising the initially proposed mining algorithms. When it comes to effectively extrapolating the most interesting knowledge nuggets from the standard output of these algorithms, one is faced with an extreme challenge, since it is not uncommon to be confronted with a vast amount of association rules after running the algorithms. The sheer multitude of generated rules often clouds the perception of the interpreters. Rightful assessment of the usefulness of the generated output introduces the need to effectively deal with different forms of data redundancy and data being plainly uninteresting. In order to do so, we will give a tentative overview of some of the main post-processing tasks, taking into account the efforts that have already been reported in the literature. (original abstract)
Rocznik
Numer
Strony
159--173
Opis fizyczny
Twórcy
Bibliografia
  • Agrawal R. & Shafer J.: „Parallel Mining of Association Rules", In IEЕЕ Knowledge & Data Engineering, 8(6), 1996.
  • Agrawal R. & Srikant R.: „Fast Algorithms for Mining Association rules", In Proc. of the 20th Int'l Conf. on Very Large Databases, Santiago, Chile 1994.
  • Agrawal R., Imielinski T. & Swami A.: fining Association Rules between Sets of Items in Massive Databases", In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Washington D.C, USA 1993.
  • Ali K, Manganane S. & Srikant R.: „Partial Classification Using Association Rules", In Proc. of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, CA, USA 1997.
  • Bayardo Jr. R.J. & Agrawal R.: „Mining the Most Interesting Rules", In Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 1999.
  • Brijs T., Vanhoof K. & Wets G.: „Reducing Redundancy in Characteristic Rule Discovery by Using IP-Techniques", Limburgs Universitair Centrum, ITEO No. 99/03, 1999.
  • Brin S., Motwani R., Ullman J.D. and Tsur S.: dynamic itemset counting and implication rules for market basket data", In Proc. of the ACM SIGMOD Conference on Management of Data, 1997.
  • Fayyad U.M., Piatetsky-Shapiro G., Smyth P. & Uthurusamy: Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996.
  • Fukuda T., Morimoto Y., Morishita S. & Tokuyama T.: „Data Mining Using Two Dimensional Optimized Association Rules: Scheme, Algorithms and Visualizatio", In Proc. of the 1996 ACM SIGMOD Conference, Montreal, Quebec, Canada, 1996.
  • Guillaume S., Guillet F. & Philipp J.: „Contribution of the integration of intensity of implication into the algorithm proposed by Agrawar, In Biennal European Meeting on Cybernetics and Systems Research, Vienna, 1998.
  • Han J.: „Towards On-line Analytical Mining in large Databases", SIGMOD Record, Vol. 27, No. 1, 1998.
  • Klemettinen M., Mannila H., Ronkainen P., Toivonen H. & Verkamo A.I.: „Finding interesting rules from large sets of discovered association rules", In Proc. of the Third International Conference on Information and Knowledge Management (CIKM'94), Gaithersburg, Maryland, USA, 1994.
  • Korn F., Labrinidis A., Kotidis Y. & Faloutsos C.: „Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining", In Proc. of the 24th VLDB Conference, New York, USA, 1998.
  • Liu В., Hsu W. & Ma Y.: „Mining Association Rules with Multiple Minimum Supports", ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-99), San Diego, CA, USA, 1999.
  • Liu В., Hsu W. & Ma Y.: „Pruning and Summarizing the Discovered Associations", ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-99), San Diego, CA, USA, 1999.
  • Liu В., Hsu W., Wang К. & Chen S.: „Visually Aided Exploration of Interesting Association Rules", In Proc. of the Pacific—Asia Conf. on Knowledge Discovery and Data Mining 1999 (PAKDD'99), Beijing, China, 1999.
  • Park J., Chen M. & Yu P.: „An effective hash based algorithm for mining association rules". In SIGMOD Conf., 1995.
  • Pei J., Hań J. & Yin Y.: „Mining Access Patterns efficiently from Weg logs", In Proc. of the Pacific-Asia Conf. on Knowledge Discovery and Data Mining 2000 (PAKDD'OO), Kyoto, Japan, April 2000.
  • Shah D., Lakshmanan L.V.S., Sudarshan S. & Ramamritham K.: „Interestingness and Pruning of Mined Patterns", In Proc. of the 1999 ACM SIGMÛD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), Philadelphia, Penn, 1999.
  • ] Silverstein C., Brin S. & Motwani R.: „Beyond Market Baskets; Generalizing Association Rules to Dependence Rules'", Data Mining and Knowledge Discovery, 2, 1998.
  • Srikant R. & Agrawal R.: „Mining Generalized Association Rules", In Proc. 1995 Int. Conf. Very Large Data Bases, Zurich, Switzerland, 1995.
  • Srikant R. & Agrawal R.: „Mining Quantitative Association Rules in large Relational Tables", In Proc. of the 1996 ACM SIGMOD Conference, Montreal, Quebec, Canada, 1996.
  • Toivonen H., Klemettinen M., Ronkainen P., Htnen K. & Mannila H.: „Pruning and Grouping of Discovered Association Rules", In Mlnet Workshop on Statistics, Machine Learning, and Discovery in Databases, Heraklion, Crete, Greece, 1995.
  • Viveros M.S., Nearhos J.P. & Rothman M.J.: „Applying Data Mining Techniques to a Health Insurance Information System", In Proceedings of the 22nd VLDB Conference, 1996.
  • Zaki M., Parthasarathy S., Ogihara M. & Li W.: „New Algorithms for fast discovery of association rules'", TR 651, CS Dept, Univ. of Rochester, 1997.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000054288922

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