Sematic Web Mining Using Fuzzy C-means Algorithm

Abstract

Semantic web mining (SWM) is the incorporation of two astonishing development research areas; semantic web and data mining. Semantic web can promote the performances and productivities of web mining. Also, data mining approaches can be applied on the semantic web data because the semantic web data is prosperous sources of knowledge to feed the data mining techniques. In this research a SWM system is designed and implemented using fuzzy C-means (FCM) algorithm. This is performed by developing an application that is created using several techniques. The system makes up of creating the semantic web dataset, dataset query (SPARQL) and converting the semantic web dataset into traditional dataset. After that, data mining is implemented encompassing data pre-processing, fuzzy C-means algorithm and finally exploring the results.  SWM using FCM has been practiced by producing an application, which involves various techniques such as DotNetRDF, C# programming language, SPARQL for query language. The final results that obtained from the achieved system are more accurate and knowledgeable because of the combination between semantic web and Fuzzy C-means.