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C. Xu and J. Wang, “An Efficient Incremental Algorithm for Frequent Itemsets Mining in Distorted Databases with Granular Computing,” Proceedings of 2006 IEEE/WIC/ ACM International Conference on Web Intelligence, Hong Kong, 2006, pp. 913-918.

has been cited by the following article:

• TITLE: DARM: Decremental Association Rules Mining

  AUTHORS: Mohamed Taha, Tarek F. Gharib, Hamed Nassar

  KEYWORDS: Decremental Mining, Association Rules Maintenance, Updating Association Rules

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.3 No.3, August 24, 2011

  ABSTRACT: Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques were proposed for maintenance of the discovered rules when new transactions are added, little work is done for maintaining the discovered rules when some transactions are deleted from the database. Updates are fundamental aspect of data management. In this paper, a decremental association rules mining algorithm is present for updating the discovered association rules when some transactions are removed from the original data set. Extensive experiments were conducted to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm is efficient and outperforms other well-known algorithms.