S. Thomas, S. Bodagala, K. Alsabti, and S. Ranka. An ecient algorithm for the incremental updation of association rules in large databases. In Proceedings of 3rd International Conference on Knowledge Discovery in Databases, 1997. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Composition of Mining Contexts for Efficient.. - Diop, Giacometti.. (2001) (Correct)
....the frequent queries for the whole set. So far, in this work, we assume that the database is static, which is not the case in practice. Therefore, an important issue is to consider updates. This problem is clearly similar to the problem of maintenance of association rules, already considered in [5, 19]. ....
S. Thomas, S. Bodagala, K. Alsabti and Sanjay Ranka (1997). An Ecient Algorithm for the Incremental Updation of Association Rules in Large Databases. Proc. of International Conference on Data Mining and Knowledge Discovery (KDD'97), pp. 263-266, Newport Beach, USA.
TopCat: Data Mining for Topic Identification in a Text Corpus - Clifton, Cooley, Rennie (2002) (6 citations) (Correct)
....ed. 7.2 Future Work One key problem we face is the continuity of topics over time. There are two issues here: Performance: Can we incrementally update the topics without looking at all the old data The data mining community is addressing this for association rules (for two examples, see [36] and [37] this should apply directly to TopCat. New knowledge: How do we alert the user when something interesting has changed We nd the latter issue to be the greater challenge. There are two types of changes: New topics, and new information added to a topic. For frequent itemsets, this ....
Shiby Thomas, Sreenath Bodagala, Khaled Alsabti, and Sanjay Ranka, \An ecient algorithm for the incremental updation of association rules in large databases," in Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Aug. 14-17 1997, pp. 263-266.
Incremental Mining of Constrained Associations - Thomas, Chakravarthy (1998) (6 citations) Self-citation (Thomas) (Correct)
....data is used to compute frequent itemsets that need to be added or deleted when the underlying data changes. This paper generalizes the use of incremental mining techniques to association rule mining with constraints. The negative border concept [Toi96] is used as the basis of incremental mining [TBAR97, FAAM97] By viewing the negative border concept as a constraint relaxation technique, incremental data mining can be readily used to eciently mine association rules with various types of constraints. We divide the constraints into four categories and show how they are used in the algorithm based ....
....of all minimal non frequent itemsets (also termed the negative border [Toi96] is also counted. This computation can be e ectively used for maintaining the frequent itemsets when the transaction database is updated. The algorithm for the incremental maintenance of frequent itemsets presented in [TBAR97] utilizes the work spent in counting the support of itemsets in the negative border. All the itemsets in the negative border along with their support counts are maintained. It has been proved that for an itemset s which was not originally present in the frequent set or the negative border to ....
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Shiby Thomas, Sreenath Bodagala, Khaled Alsabti, and Sanjay Ranka. An Ecient Algorithm for the Incremental Updation of Association Rules in Large Databases. In Proc. of the 3rd Int'l Conference on Knowledge Discovery and Data Mining, Newport Beach, California, August 1997. 21
Thesis Proposal - Ruoming Jin Department (Correct)
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S. Thomas, S. Bodagala, K. Alsabti, and S. Ranka. An ecient algorithm for the incremental updation of association rules in large databases. In Proceedings of 3rd International Conference on Knowledge Discovery in Databases, 1997.
Mining the Customer's Up-To-Moment Preferences for.. - Shen, Yang, Zhang, Lu (Correct)
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S. Thomas, S. Bodagala, K. Alsabti, and S. Ranka. An ecient algorithm for the incremental updation of association rules in large databases. In KDD, pages 263-266, 1997.
Algorithms for Association Rule Mining - A General.. - Hipp, Güntzer.. (2000) (51 citations) (Correct)
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S. Thomas, S. Bodagala, K. Alsabti, and S. Ranka. An ecient algorithm for the incremental updation of association rules in large databases. In Proc. of the 3rd Int'l Conf. on KDD and Data Mining (KDD '97), Newport Beach, California, August 1997.
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