Aggregating Multiple Instances in Relational Database Using Semi-Supervised Genetic Algorithm-based Clustering Technique

The 9th annual meeting of the Society of Japanese Photo-Aging Research

Rayner Alfred ; Dimitar Kazakov

Others  :  http://CEUR-WS.org/Vol-325/paper13.pdf
PID  :  2641

来源: CEUR

【 摘 要 】

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.