Name
Abstract | ||
|---|---|---|
Attribute-Oriented Induction (AOI) is a set-oriented data mining technique used to discover descriptive patterns in large
databases. The classical AOI method drops attributes that possess a large number of distinct values or have either no concept
hierarchies, which includes keys to relational tables. This implies that the final rule (s) produced have no direct link to
the tuples that form them. Therefore the discovered knowledge cannot be used to efficiently query specific data pertaining
to this knowledge in a different relation to the learning relation.
This paper presents the key-preserving AOI algorithm (AOI-KP) with two implementation approaches. The order complexity of
the algorithm is O (np), which is the same as for the enhanced AOI algorithm where n and p are the number of input and generalised tuples respectively. An application of the method is illustrated and prototype tool
support and initial results are outlined with possible improvements.
|
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/978-3-540-48247-5_57 | PKDD |
Keywords | Field | DocType |
key-preserving data mining method,attribute-oriented induction,data mining | Information system,Dynamic array,Data mining,Information processing,Relational database,Tuple,Attribute oriented induction,Computer science,Hierarchy,Knowledge acquisition | Conference |
Volume | ISSN | ISBN |
1704 | 0302-9743 | 3-540-66490-4 |
Citations | PageRank | References |
6 | 0.50 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maybin K. Muyeba | 1 | 47 | 7.61 |
| John A. Keane | 2 | 695 | 92.81 |