Ontology-based clustering has gained attention in recent years due to the potential benefits of ontology. Current ontology-based clustering approaches have mainly been applied to reduce the dimensionality of attributes in text document clustering. Reduction in dimensionality of attributes using ontology helps to produce high quality clusters for a dataset. However, ontology-based approaches in clustering numerical datasets have not been gained enough attention. Moreover, some literature mentions that ontology-based clustering can produce either high quality or low-quality clusters from a dataset. Therefore, in this paper we present a clustering approach that is based on domain ontology to reduce the dimensionality of attributes in a numerical dataset using domain ontology and to produce high quality clusters. For every dataset, we produce three datasets using domain ontology. We then cluster these datasets using a genetic algorithm-based clustering technique called GenClust++. The clusters of each dataset are evaluated in terms of Sum of Squared-Error (SSE). We use six numerical datasets to evaluate the performance of our ontology-based approach. The experimental results of our approach indicate that cluster quality gradually improves from lower to the higher levels of a domain ontology.

翻译：基于本体的聚类方法近年来因其潜在优势而受到关注。当前的基于本体的聚类方法主要应用于文本文档聚类中的属性降维。利用本体进行属性降维有助于为数据集生成高质量聚类。然而，基于本体的方法在数值数据集聚类中尚未得到足够重视。此外，部分文献提及基于本体的聚类可能产生高质量或低质量聚类结果。因此，本文提出一种基于领域本体的聚类方法，通过领域本体降低数值数据集的属性维度，并生成高质量聚类。针对每个数据集，我们利用领域本体生成三个子数据集，随后采用基于遗传算法的聚类技术GenClust++对这些数据集进行聚类。各数据集的聚类效果通过误差平方和（SSE）进行评估。我们使用六个数值数据集验证该本体方法的性能。实验结果表明，聚类质量随领域本体层级的提升而逐步改善。