Name
Abstract | ||
|---|---|---|
Missing values issue in databases is an important problem because missing values bias the information provided by the usual data mining methods. In this paper, we are searching for mining patterns satisfying correct properties in presence of missing values (it means that these patterns must satisfy the properties in the corresponding complete database). We focus on k-free patterns. Thanks to a new definition of this property suitable for incomplete data and compatible with the usual one, we certify that the extracted k-free patterns in an incomplete database also satisfy this property in the corresponding complete database. Moreover, this approach enables to provide an anti-monotone criterion with respect to the pattern inclusion and thus design an efficient level-wise algorithm which extracts correct k-free patterns in presence of missing values. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/978-3-540-75549-4_13 | KDID |
Keywords | Field | DocType |
missing values issue,correct k-free pattern,missing values bias,incomplete database,mining pattern,incomplete data,incomplete databases,missing value,k-free pattern,correct property,corresponding complete database,satisfiability,missing values,data mining | Data mining,Association rule learning,Imputation (statistics),Missing data,Mathematics,Database | Conference |
Volume | ISSN | ISBN |
4747 | 0302-9743 | 3-540-75548-9 |
Citations | PageRank | References |
1 | 0.36 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| François Rioult | 1 | 92 | 13.23 |
| Bruno Crémilleux | 2 | 373 | 34.98 |