Add to ORKGInternational audienceThis article extends the method of Garriga et al. for mining relevant rules to numerical attributes by extracting interval-based pattern rules. We propose an algorithm that extracts such rules from numerical datasets using the interval-pattern approach from Kaytoue et al. This algorithm has been implemented and evaluated on real datasets
Abstract. Most methods for temporal pattern mining assume that time is represented by points in a st...
Association rule mining is a popular technique used to find associations between attributes in a dat...
International audienceMost methods for temporal pattern mining assume that time is represented by po...
International audienceThis article extends the method of Garriga et al. for mining relevant rules to...
We consider the problem of mining association rules over interval data (that is, ordered data for wh...
Discovering association rules is a classical data mining task with a wide range of applications that...
The standard formulation of association rules is suitable for describing patterns found in a given d...
Abstract. Recently a new type of data source came into the focus of knowledge discovery from tempora...
Association rule mining typically targets transactional data. In order to process non-transaction da...
The ability to learn classification rules from data is important and useful in a range of applicatio...
The ability to learn classification rules from data is important and useful in a range of applicatio...
The ability to learn classification rules from data is important and useful in a range of applicatio...
In traditional association rule mining algorithms, if the minimum support is set too high, many valu...
Discovering association rules is a classical data mining task with a wide range of applications that...
Granular association rule mining is a new relational data mining approach to reveal patterns hidden ...
Abstract. Most methods for temporal pattern mining assume that time is represented by points in a st...
Association rule mining is a popular technique used to find associations between attributes in a dat...
International audienceMost methods for temporal pattern mining assume that time is represented by po...
International audienceThis article extends the method of Garriga et al. for mining relevant rules to...
We consider the problem of mining association rules over interval data (that is, ordered data for wh...
Discovering association rules is a classical data mining task with a wide range of applications that...
The standard formulation of association rules is suitable for describing patterns found in a given d...
Abstract. Recently a new type of data source came into the focus of knowledge discovery from tempora...
Association rule mining typically targets transactional data. In order to process non-transaction da...
The ability to learn classification rules from data is important and useful in a range of applicatio...
The ability to learn classification rules from data is important and useful in a range of applicatio...
The ability to learn classification rules from data is important and useful in a range of applicatio...
In traditional association rule mining algorithms, if the minimum support is set too high, many valu...
Discovering association rules is a classical data mining task with a wide range of applications that...
Granular association rule mining is a new relational data mining approach to reveal patterns hidden ...
Abstract. Most methods for temporal pattern mining assume that time is represented by points in a st...
Association rule mining is a popular technique used to find associations between attributes in a dat...
International audienceMost methods for temporal pattern mining assume that time is represented by po...