Efficient Mining of Dissociation Rules
Association rule mining is one of the most popular data mining techniques. Significant work has been done to extend the basic association rule framework to allow for mining rules with negation. Negative association rules indicate the presence of negative correlation between items and can reveal valuable knowledge about examined dataset. Unfortunately, the sparsity of the input data significantly reduces practical usability of negative association rules, even if additional pruning of discovered rules is performed. In this paper we introduce the concept of dissociation rules. Dissociation rules present a significant simplification over sophisticated negative association rule framework, while keeping the set of returned patterns concise and actionable. A new formulation of the problem allows us to present an efficient algorithm for mining dissociation rules. Experiments conducted on synthetic datasets prove the effectiveness of the proposed solution.
Unable to display preview. Download preview PDF.
- 1.Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1996)Google Scholar
- 3.Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: 1993 ACM SIGMOD, Washington, DC, May 26-28, 1993, pp. 207–216 (1993)Google Scholar
- 4.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, Santiago de Chile, September 12-15, 1994, pp. 487–499 (1994)Google Scholar
- 5.Amir, A., Feldman, R., Kashi, R.: A new versatile method for association generation. In: Princ. of Data Mining and Knowledge Disc., pp. 221–231 (1997)Google Scholar
- 6.Antonie, M.-L., Zaiane, O.R.: Mining positive and negative association rules: An approach for confined rules. Technical Report TR04-07, Department of Computing Science, University of Alberta (2004)Google Scholar
- 11.Padmanabhan, B., Tuzhilin, A.: A belief-driven method for discovering unexpected patterns. In: KDD 1998, New York, August 27-31, 1998, pp. 94–100. AAAI Press, Menlo Park (1998)Google Scholar
- 12.Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT Press (1991)Google Scholar
- 13.Savasere, A., Omiecinski, E., Navathe, S.B.: Mining for strong negative associations in a large database of customer transactions. In: ICDE 1998, Orlando, Florida, USA, February 23-27, 1998, pp. 494–502. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar