A Multi-objectives Genetic Algorithm Clustering Ensembles Based Approach to Summarize Relational Data

In learning relational data, the Dynamic Aggregation of Relational Attributes algorithm is capable to transform a multi-relational database into a vector space representation, in which a traditional clustering algorithm can then be applied directly to summarize relational data. However, the performance of the algorithm is highly dependent on the quality of clusters produced. A small change in the initialization of the clustering algorithm parameters may cause adverse effects to the clusters quality produced. In optimizing the quality of clusters, a Genetic Algorithm is used to find the best combination of initializations in order to produce the optimal clusters. The proposed method involves the task of finding the best initialization with respect to the number of clusters, proximity distance measurements, fitness functions, and classifiers used for the evaluation. Based on the results obtained, clustering coupled with Euclidean distance is found to perform better in the classification stage compared to using clustering coupled with Cosine similarity. Based on the findings, the cluster entropy is the best fitness function, followed by multi-objectives fitness function used in the genetic algorithm. This is most probably because of the involvement of external measurement that takes the class label into consideration in optimizing the structure of the cluster results. In short, this paper shows the influence of varying the initialization values on the predictive performance.