The analysis of relationships in databases for rule derivation
Journal of Intelligent Information Systems
Principles of Database Systems
Principles of Database Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
| Hi-index | 0.00 |
Over the past decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion has pointed out the need of techniques or algorithms in order to extract and discover non-trivial, unknown and potentially useful information from large data sets. This extraction of knowledge from large data sets is called Data Mining or Knowledge Discovery in Databases. The extracted knowledge can be used to answer cooperative queries, and facilitate semantic query optimization. Relational databases create new type of problems for knowledge discovery such as missing values for some attributes and a key issue in any discovery system is to ensure the completeness of the discovered knowledge. In this paper, we address the problem of missing values in relational databases. We present an approach to complete or augment a classical relation containing missing values. This is done by exploiting the useful information yielded by the discovered knowledge represented by formal concepts.