Data mining extracts implicit, previously unknown and potentially useful information from databases. Many approaches have been proposed to extract information, and one of the most ...
Association rules can reveal biological relevant relationship between genes and environments / categories. However, most existing association rule mining algorithms are rendered i...
Although the task of mining association rules has received considerable attention in the literature, algorithms to find time association rules are often inadequate, by either miss...
Recommendation systems often use association rules as main technique to discover useful links among the set of transactions, especially web usage data – historical user sessions....
In this paper, we present PRICES, an efficient algorithm for mining association rules, which first identifies all large itemsets and then generates association rules. Our approach ...
The effort of data mining, especially in relation to association rules in real world business applications, is significantly important. Recently, association rules algorithms have ...
Abstract. Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, int...
We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. S...
This paper presents a framework for user-oriented text mining. It is then illustrated with an example of discovering knowledge from competitors’ websites. The knowledge to be di...
The notion of rules is very popular and appears in different flavors, for example as association rules in data mining or as functional (or multivalued) dependencies in databases. ...