Rayner Alfred and Dimitar Kazakov (2007) Aggregating multiple instances in relational database using semi-supervised genetic algorithm-based clustering technique. In: Communications of the Eleventh East-European Conference on Advances in Databases and Information Systems, September 29 - October 3, 2007, Varna, Bulgaria.
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Abstract
In solving the classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets stored in multiple tables into a single table. Unfortunately, we may loss some information when we join tables with a high degree of one-to-many association. Therefore, data transformation becomes a tedious trial-and-error work and the classification result is often not very promising especially when the number of tables and the degree of one-to-many association are large. In this paper, we propose a genetic semi-supervised clustering technique as a means of aggregating data in multiple tables for the classification problem in relational database. This algorithm is suitable for classification of datasets with a high degree of one-to-many associations. It can be used in two ways. One is user-controlled clustering, where the user may control the result of clustering by varying the compactness of the spherical cluster. The other is automatic clustering, where a non-overlap clustering strategy is applied. In this paper, we use the latter method to dynamically cluster multiple instances, as a means of aggregating them, and illustrate the effectiveness of this method using the semi-supervised genetic algorithm-based clustering technique.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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| Keyword: | Aggregation, Clustering, Semi-supervised clustering, Genetic Algorithm, Relational data Mining |
| Subjects: | ?? QA75 ?? |
| Department: | FACULTY > Faculty of Computing and Informatics |
| Depositing User: | ADMIN ADMIN |
| Date Deposited: | 17 Nov 2015 15:20 |
| Last Modified: | 10 Nov 2017 09:42 |
| URI: | https://eprints.ums.edu.my/id/eprint/12312 |
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