Sequence mining in categorical domains: incorporating constraints
By Mohammed J. Zaki. International Conference on Information and Knowledge Management, 2000
325 citations recorded by the source corpus.
We present cSPADE, an ecient algorithm for mining fre- quent sequences considering a variety of syntactic constraints. These take the form of length or width limitations on the sequences, minimum or maximum gap constraints on con- secutive sequence elements, applying a time window on al- lowable sequences, incorporating item constraints, and Þnd- ing sequences predictive of one or more classes, even rare ones. Our method is ecient and scalable. Experiments on a number of synthetic and real databases show the utility and performance of considering such constraints on the set of mined sequences.
Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/32ef20f85d90e084ae06f99c44d05ba9965964a4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Information Systems
In Signal Processing
Citations in the corpus
- Sequential pattern mining -- approaches and algorithms (2013, methodology): “The algorithm is similar to GSP [Srikant and Agrawal 1996] because it adopts a level-wise bottom-up approach in visiting the sequential patterns in the tree but it differs since after extracting the frequent F1 and F2 from the horizontal database, this pruned database is transformed into a vertic”
- Constraint-based Data Mining (2005, methodology): “Interestingly, new constraints can exploit the spatial or temporal order, e.g., the min-gap and max-gap constraints (see, e.g., (Zaki, 2000) and (Pei et al ., 2002) for a recent survey).”