Name
Title | ||
|---|---|---|
Interestingness in Attribute-Oriented Induction (AOI): Multiple-Level Rule Generation |
Abstract | ||
|---|---|---|
Attribute-Oriented Induction (AOI) is a data mining technique that produces simplified descriptive patterns. Classical AOI uses a predictive strategy to determine distinct values of an attribute but generalises attributes indiscriminately i.e. the value 'ANY' is replaced like any other value without restrictions. AOI only produces interesting rules by using interior concepts of attribute hierarchies. The COMPARE algorithm that integrates predictive and lookahead methods and of order complexity O(np), where n and p are input and generalised tuples respectively, is introduced. The latter method determines distinct values of attribute clusters and greatest number of attribute values with a 'common parent' (except parent 'ANY'). When generating rules, a rough set approach to eliminate redundant attributes is used leading to more interesting multiple-level rules with fewer 'ANY' values than classical AOI. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-45372-5_64 | PKDD |
Keywords | Field | DocType |
redundant attribute,attribute value,predictive strategy,attribute-oriented induction,attribute cluster,distinct value,interesting rule,multiple-level rule generation,interesting multiple-level rule,attribute hierarchy,classical aoi,common parent,rough set,data mining | Data mining,Information processing,Relational database,Algorithm complexity,Computer science,Attribute oriented induction,Tuple,Rough set,Hierarchy,Concept hierarchy | Conference |
Volume | ISSN | ISBN |
1910 | 0302-9743 | 3-540-41066-X |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maybin K. Muyeba | 1 | 47 | 7.61 |
| John A. Keane | 2 | 695 | 92.81 |