%0 Journal Article %1 cabrelefficient %A Cabrel, Tabueu Fotso Laurent %D 2022 %J BOHR International Journal of Computer Science %K Partitioning adjacencymatrix lattice&Patternmining maximalgradualsupport pruningsearchspace %N 1 %P 11- 25 %R 10.54646/BIJCS.003 %T An Efficient Hybrid by Partitioning Approach for Extracting Maximal Gradual Patterns in Large Databases (MPSGrite) %U https://journals.bohrpub.com/index.php/bijcs/article/view/72 %V 2 %X Since automatic knowledge extraction must be performed in large databases, empirical studies are already showing an explosion in the search space for generalized patterns & even more so for frequent gradual patterns. In addition to this we also observe a generation of a very large number of relevant extracted patterns. Faced with this problem, many approaches have been developed, with the aim of reducing the size of the search space & by the waiting time for detection, for end users, of relevant patterns. the objective is to make decisions or refine their analyzes within a reasonable & realistic time frame. The gradual pattern mining algorithms common in large databases are CPU intensive. It is a question for us of proposing a new approach which allows an extraction of the maximum frequent gradual patterns based on a technique of partitioning data sets. MSPGrite, a new, more efficient hybrid algorithm, is the result of the new method. Experiments on numerous sets of well-known datasets support the suggested method

@article{cabrelefficient, abstract = {Since automatic knowledge extraction must be performed in large databases, empirical studies are already showing an explosion in the search space for generalized patterns & even more so for frequent gradual patterns. In addition to this we also observe a generation of a very large number of relevant extracted patterns. Faced with this problem, many approaches have been developed, with the aim of reducing the size of the search space & by the waiting time for detection, for end users, of relevant patterns. the objective is to make decisions or refine their analyzes within a reasonable & realistic time frame. The gradual pattern mining algorithms common in large databases are CPU intensive. It is a question for us of proposing a new approach which allows an extraction of the maximum frequent gradual patterns based on a technique of partitioning data sets. MSPGrite, a new, more efficient hybrid algorithm, is the result of the new method. Experiments on numerous sets of well-known datasets support the suggested method}, added-at = {2022-07-09T15:08:16.000+0200}, author = {Cabrel, Tabueu Fotso Laurent}, biburl = {https://www.bibsonomy.org/bibtex/211d4d0184b2ab1cf9b25dcfcc57c0f01/bijcs}, doi = {10.54646/BIJCS.003}, interhash = {e1ce4e8531c9d0b2113578bb0d2a9ca8}, intrahash = {11d4d0184b2ab1cf9b25dcfcc57c0f01}, journal = {BOHR International Journal of Computer Science}, keywords = {Partitioning adjacencymatrix lattice&Patternmining maximalgradualsupport pruningsearchspace}, language = {English}, month = {February}, number = 1, pages = {11- 25}, timestamp = {2023-01-23T12:59:32.000+0100}, title = {An Efficient Hybrid by Partitioning Approach for Extracting Maximal Gradual Patterns in Large Databases (MPSGrite)}, url = {https://journals.bohrpub.com/index.php/bijcs/article/view/72}, volume = 2, year = 2022 }