By the same authors

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

Research output: Contribution to conferencePaper

Author(s)

Department/unit(s)

Publication details

DatePublished - 2007
Original languageUndefined/Unknown

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.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations