Thusaranon, Panita
Loading...
Preferred name
Thusaranon, Panita
Email
panita.th@kmitl.ac.th
2 results
Now showing 1 - 2 of 2
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, A probability‑based incremental association rule discovery algorithm for record insertion and deletion(2015-07-01); The maintenance of association rules for dynamic database is an important problem because the updates may not only invalidate some existing rules but also make other rules relevant. This paper is the extension work of probability-based incremental association rule discovery algorithm which can only handle new data insert into a dynamic database. Unlike the previous work, the proposed algorithm can efficiently handle in case of insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains the support counts of frequent itemsets and promising frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Promising frequent itemsets, which are obtained by using the principle of Bernoulli trials, can help to reduce a number of times to rescan the original database. The support counts of new candidate itemsets are approximated by using the principle of maximum possible value. The experimental results show that the execution time of the proposed algorithm is faster than that of Apriori, FUP2, EDUA, and pre-large algorithm. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Frequent itemsets mining using random walks for record insertion and deletion(2017-02-23); In Association rules mining, the task of finding frequent itemsets in dynamic database is very important because the updates may not only invalidate some existing rules but also make other rules relevant. In this paper, we propose a new algorithm to maintain frequent itemsets of a dynamic database in the case of record insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains not only the support counts of frequent itemsets but also the support counts of prospective frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Prospective frequent itemsets, which are obtained by using the principle of Random Walks, can help to reduce a number of times to rescan the original database.