Data Fusion Based on Self-Organizing Map Approach to Learning Medical Relational Data

Abstract

Amount of data generated and stored in relational databases has motivated numerous researchers to study and develop learning algorithms on learning relational data mining. One of the most important relational tasks is to discover knowledge from relational data for a better decision making. Despite that, various representations can be generated using the same data by applying the Self-Organizing Map (SOM) methods in clustering relational data. This can be achieved by tuning the parameters used in Self-Organizing Map (SOM), such as the number of clustering, weights, seeds, epoch and others. Thus, this paper proposes a summarization method that applies SOM as the main algorithm to cluster relational data and applies the concept of data fusion in order to get better results in learning relational data. Input data obtained from Dynamic Aggregation of Relational Attributes will be clustered using the SOM method by tuning the SOM parameters. Results generated will be fused and embedded into the target table to form a single representation. A few representations will be formed and fed into the classifiers (J48 Decision Tree and Naïve Bayes classification model) as input data. Throughout the experiments conducted, representations that are extracted by tuning the number of cluster produced better results compared to the representations that are extracted by tuning the other parameters. Overall, the data summarization approach based on individual data fusion is found to perform better compared to the other types of data fusion. In addition to that, the clusters based data fusion with average number of clusters provided better accuracy performances compared to clusters based data fusion with small and large number of clusters.