Improved Negative-Border Online Mining Approaches
In the past, we proposed an extended multidimensional pattern relation (EMPR) to structurally and systematically store previously mining information for each inserted block of data, and designed a negative-border online mining (NOM) approach to provide ad-hoc, query-driven and online mining supports. In this paper, we try to use appropriate data structures and design efficient algorithms to improve the performance of the NOM approach. The lattice data structure is utilized to organize and maintain all candidate itemsets such that the candidate itemsets with the same proper subsets can be considered at the same time. The derived lattice-based NOM (LNOM) approach will require only one scan of the itemsets stored in EMPR, thus saving much computation time. In addition, a hashing technique is used to further improve the performance of the NOM approach since many itemsets stored in EMPR may be useless for calculating the counts of candidates. At last, experimental results show the effect of the improved NOM approaches.
KeywordsExecution Time Association Rule Hash Table Minimum Support Synthetic Dataset
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- 2.Agrawal, R., Srikant, R.: Fast Algorithm for Mining Association Rules. In: The ACM International Conference on Very Large Data Bases, pp. 487–499 (1994) Google Scholar
- 4.Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Approach. In: The IEEE International Conference on Data Engineering, pp. 106–114 (1996)Google Scholar
- 6.Hong, T.P., Wang, C.Y., Tao, Y.H.: A New Incremental Data Mining Algorithm Using Pre-large Itemsets. An International Journal: Intelligent Data Analysis, 111–129 (2001)Google Scholar
- 7.Immon, W.H.: Building the Data Warehouse. Wiley Computer Publishing, Chichester (1996)Google Scholar
- 8.Mannila, H., Toivonen, H.: On an Algorithm for Finding All Interesting Sentences. In: The European Meeting on Cybernetics and Systems Research, pp. 973–978 (1996)Google Scholar
- 10.Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An Efficient Algorithm for the Incremental Update of Association Rules in Large Databases. In: The International Conference on Knowledge Discovery and Data Mining, pp. 263–266 (1997)Google Scholar
- 11.Wang, C.Y., Tseng, S.S., Hong, T.P.: Flexible Online Association Rule Mining Based on Multidimensional Pattern Relations. An International Journal: Information Sciences (2005) (to appear)Google Scholar
- 12.Wang, C.Y., Tseng, S.S., Hong, T.P., Chu, Y.S.: Online Generation of Association Rules under Multidimensional Consideration Based on Negative-border. Journal of Information Science and Engineering (2005) (to appear)Google Scholar
- 13.Widom, J.: Research Problems in Data Warehousing. In: The ACM International Conference on Information and Knowledge Management, pp. 25–30 (1995)Google Scholar