Access to huge data is needed for an appropriate structure and grouping of data such that the access to the data becomes easier, the status which clustering algorithms are doing this for us. However, special attentions are paid in recent years on semantic data clustering which in semantic interpretation of the input data is needed. In this paper, three modified clustering methods are used and the results of these techniques are evaluated. Based on this, first a technique is developed in which some rules are applied to prevent confusion within clusters. A rule-based clustering can be applied to the given data. Then, next technique performs these rules with applying ontology-based semantics. And last and basic technique changes presumed ontology and then rules applied on clusters. The result shows that the clusters derived from the information provided within them were very similar and very different from other clusters and a significant reduction in the k-distance of these clusters is also occurred and the correlation is increased.DOI:http://dx.doi.org/10.11591/ijece.v4i1.4078

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