About Summarization in Large Fuzzy Databases
This work addresses the need for modeling fuzzy data in large databases, offering an incremental improvement for users in data summarization systems.
The paper tackles the problem of generating summaries from fuzzy data by extending the SaintEtiQ model to create Fuzzy-SaintEtiQ, which optimizes expert risk minimization, constructs more detailed and precise summary hierarchies, and cooperates with users by providing fuzzy summaries at different hierarchical levels.
Moved by the need increased for modeling of the fuzzy data, the success of the systems of exact generation of summary of data, we propose in this paper, a new approach of generation of summary from fuzzy data called Fuzzy-SaintEtiQ. This approach is an extension of the SaintEtiQ model to support the fuzzy data. It presents the following optimizations such as 1) the minimization of the expert risk; 2) the construction of a more detailed and more precise summaries hierarchy, and 3) the co-operation with the user by giving him fuzzy summaries in different hierarchical levels