Mining first-order maximal frequent patterns
| Authors | |
|---|---|
| Year of publication | 2004 |
| Type | Article in Periodical |
| Magazine / Source | Neural Network World |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | knowledge discovery in databases; inductive logic programming; frequent patterns; feature construction; propositionalization |
| Description | Frequent patterns discovery is one of the most important data mining tasks. We introduce RAP, the first system for finding first-order maximal frequent patterns. We describe search strategies and methods of pruning the search space. RAP generates long patterns much faster than other systems.RAP has been used for feature construction for propositional as well as multirelational data. We prove that partial search for maximal frequent patterns as new features is competitive with other approaches and results in classification accuracy increase. |
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