Integrating Association Rules with Decision Trees in Object-Relational Databases

Abstract

Research has provided evidence that associative classification produces more accurate results compared to other classification models. The Classification Based on Association (CBA) is one of the famous Associative Classification algorithms that generates accurate classifiers. However, current association classification algorithms reside external to databases, which reduces the flexibility of enterprise analytics systems. This paper implements the CBA in Oracle database using two variant models-hardcoding the CBA in Oracle Data Mining (ODM) package and Integrating Oracle Apriori model with the Oracle Decision tree model. We compared the proposed model performance with Naïve Bayes, Support Vector Machine, Random Forests, and Decision Tree over 18 datasets from UCI. Results showed that our models outperformed the original CBA model with 1% and is competitive to chosen classification models over benchmark datasets.

Cache Contention on Multicore SystemsAn Ontology-based Approach

“…Fig 2. cache contention ontology model class in Figure2is from the work of Rohloff[24]. However, further refinement to this class model is needed in order to have a model that match cache contention requirements[25], using datamining techniques[26], and integrating software engineering techniques[27][28].…”