Conference Papers

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A dynamic approach for discovering maximal frequent itemsets
(2009) Geetha, M.; D'Souza, R.J.
We present a novel method, which reads the database at regular intervals as in Dynamic Itemsets Counting Technique and creates a tree called Dynamic Itemset Tree containing items which may be frequent, potentially frequent and infrequent. This algorithm requires less time to discover all maximal frequent itemsets since it involves a method for reducing the size of the database. This method prunes the transactions and items of the transactions which are not of our interest after every scan of the database. Also, this method is independent of the order of the items. Â© 2009 IEEE.
Discovery of weighted association rules mining
(2010) Kumar, P.; Ananthanarayana, V.S.
Mining of association rules for basket databases, has been investigated by [1] [3] [4], [9], [12], etc. Most of these works focus on mining binary association rules, i.e, most of the association rules mining algorithms to discover frequent itemsets do not consider the quantity in which items have been purchased. This paper discusses an efficient method for discovering a weighted association rules from a large volumes of data in a single scan of the database. The data structure used here is called Weighted Tree. We found that this algorithm is more efficient than Cai's Algorithm. Â©2010 IEEE.