Mining RDF (Linked Data) using Éclat algorithm

Wria Mohammed Salih Mohammed

doi:10.25098/3.2.21

Authors

Wria Mohammed Salih Mohammed

DOI:

https://doi.org/10.25098/3.2.21

Keywords:

RDF, Data Mining, Linked Data, Éclat, SPARQL

Abstract

Basket market analysis is one of the most widely used groups of data mining and have been extensively utilized for analyzing data to extract interesting information from huge amount of data. Also, Studies over the past two decades have provided important information on semantic web as it is part of (World Wide Web Consortium) W3C. Both data mining and semantic web have several key features to mine semi-structured dataset and having an accurate result. The methodological approach taken in this study is combining both Éclat algorithm and RDF (Resource Description Framework) dataset based on the process of converting RDF into dataset and mining it. Firstly, RDF data is checked for validation, and then it needs to convert into traditional dataset. This process requests SPARQL as a query language. Thus, it needs to imply Éclat (Equivalence class Transformation) algorithm on traditional dataset. This experiment illustrates that semantic web and data mining have significant results in mining semi-structured dataset. This paper hands out how mixing RDF and Éclat algorithm is influent. For this technique different data source can be used, however, for this paper particularly products in a supermarket are going to use as a main dataset.

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