Name
Abstract | ||
|---|---|---|
This paper presents a framework to mine summary emerging patterns in contrast to the familiar low-level patterns. Generally, growth rate based on low-level data and simple supports are used to measure emerging patterns (EP) from one dataset to another. This consequently leads to numerous EPs because of the large numbers of items. We propose an approach that uses high-level data: high-level data captures the data semantics of a collection of attributes values by using taxonomies, and always has larger support than low-level data. We apply a well known algorithm, attribute-oriented induction (AOI), that generalises attributes using taxonomies and investigate properties of the rule sets obtained by generalisation algorithms. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23878-9_21 | IDEAL |
Keywords | Field | DocType |
larger support,high-level data,low-level data,attributes value,large number,generalisation algorithm,data semantics,growth rate,attribute-oriented induction,familiar low-level pattern,algorithm,high level | Data mining,Generalization,Attribute oriented induction,Computer science,Data semantics,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
6936 | 0302-9743 | 3 |
PageRank | References | Authors |
0.38 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maybin K. Muyeba | 1 | 47 | 7.61 |
| Muhammad S. Khan | 2 | 29 | 3.72 |
| Spits Warnars | 3 | 4 | 3.74 |
| John Keane | 4 | 59 | 6.17 |