Sharing confidential data for algorithm development by multiple imputation
Sharing confidential data for algorithm development by multiple imputation
Samenvatting
The availability of real-life data sets is of crucial importance for algorithm and application development, as these often require insight into the specific properties of the data. Often, however, such data are not released because of their proprietary and confidential nature. We propose to solve this problem using the statistical technique of multiple imputation, which is used as a powerful method for generating realistic synthetic data sets. Additionally, it is shown how the generated records can be combined into networked data using clustering techniques.
Organisatie | Hogeschool Rotterdam |
Lectoraat | Kenniscentrum Creating 010 |
Gepubliceerd in | SSDBM Proceedings of the 25th International Conference on Scientific and Statistical Database Management ACM New York, New York, Vol. 2013 |
Datum | 2013-07-29 |
Type | Artikel |
ISBN | 9781450319218 |
DOI | 10.1145/2484838.2484865 |
Taal | Engels |