Name
Abstract | ||
|---|---|---|
It is well known that local patterns are at the core of a lot of knowledge which may be discovered from data. Nevertheless, use of local patterns is limited by their huge number and computational costs. Sev- eral approaches (e.g., condensed representations, pattern set discovery) aim at grouping or synthesizing local patterns to provide a global view of the data. A global pattern is a pattern which is a set or a synthesis of local patterns coming from the data. In this paper, we propose the idea of global constraints to write queries addressing global patterns. A key point is the ability to bias the designing of global patterns accord- ing to the expectation of the user. For instance, a global pattern can be oriented towards the search of exceptions or a clustering. It requires to write queries taking into account such biases. Open issues are to design a generic framework to express powerful global constraints and solvers to mine them. We think that global constraints are a promising way to discover relevant global patterns. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69848-7_99 | ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II |
Keywords | Field | DocType |
data mining,local pattern,top-k patterns.,global constraints,global pattern,top-kpatterns w,global view,account relationship,condensed representation,global constraint,generic approximate-and-push approach,constraint-based paradigm,local patterns,discovering knowledge,huge number,computational cost | Computer science,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
5073 | 0302-9743 | 9 |
PageRank | References | Authors |
0.48 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno Crémilleux | 1 | 373 | 34.98 |
| Arnaud Soulet | 2 | 241 | 28.18 |