Towards An Incremental Maintenance of Cyclic Association Rules

Towards an incremental maintenance of cyclic association rules

Recently, the cyclic association rules have been introduced in order to discover rules from items characterized by their regular variation over time. In real life situations, temporal databases are often appended or updated. Rescanning the whole database every time is highly expensive while existing incremental mining techniques can efficiently solve such a problem. In this paper, we propose an...

Incremental maintenance of generalized association rules under taxonomy evolution

Mining association rules from large databases of business data is an important topic in data mining. In many applications, there are explicit or implicit taxonomies (hierarchies) for items, so it may be useful to find associations at levels of the taxonomy other than the primitive concept level. Previous work on the mining of generalized association rules, however, assumed that the taxonomy of ...

Incremental Maintenance Of Association Rules Under Support Threshold Change

Maintenance of association rules is an interesting problem. Several incremental maintenance algorithms were proposed since the work of (Cheung et al, 1996). The majority of these algorithms maintain rule bases assuming that support threshold doesn't change. In this paper, we present incremental maintenance algorithm under support threshold change. This solution allows user to maintain its rule ...

Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique

Cyclic Association Rules

We study the problem of discovering association rules that display regular cyclic variation over time. For example, if we compute association rules over monthly sales data, we may observe seasonal variation where certain rules are true at approximately the same month each year. Similarly, association rules can also display regular hourly, daily, weekly, etc., variation that is cyclical in natur...