Name
Abstract | ||
|---|---|---|
The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support and confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. In temporal datasets as the time in which a transaction takes place is important we may find sets of items that are frequent in certain time intervals but not frequent throughout the dataset. These frequent sets may give rise to interesting rules but these can not be discovered if we calculate the supports of the item sets in the usual way. We call here these frequent sets locally frequent. Normally these locally frequent sets are periodic in nature. We propose modification to the Apriori algorithm to compute locally frequent sets and periodic frequent sets and periodic association rules. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11590316_91 | PReMI |
Keywords | Field | DocType |
frequent set,possible association,interesting rule,minimum support,certain time interval,periodic association rule,association rule,item set,apriori algorithm,periodic frequent set | Transaction processing,Data mining,Data structure,Computer science,Apriori algorithm,Association rule learning,Artificial intelligence,Database transaction,Periodic graph (geometry),Machine learning | Conference |
Volume | ISSN | ISBN |
3776 | 0302-9743 | 3-540-30506-8 |
Citations | PageRank | References |
3 | 0.43 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anjana Kakoti Mahanta | 1 | 27 | 3.22 |
| F. A. Mazarbhuiya | 2 | 3 | 0.77 |
| H. K. Baruah | 3 | 3 | 0.43 |