<![CDATA[There has been an increased demand for structured data mining. Graphs are among the most extensively researched data structures in discrete mathematics and computer science. Thus, it should come as no surprise that graph-based data mining has gained popularity in recent years. Graph-based methods for a transaction database are necessary to transform all the information into a graph form to conveniently extract more valuable information to improve the decision-making process. Graph-based data mining can reveal and measure process insights in a detailed structural comparison strategy that is ready for further analysis without the loss of significant details. This paper analyzes the similarities and differences among four of the most popular graph-based methods that is applied to mine rules from transaction databases by abstracting them out as a concrete high-level interface and connecting them into a common space.]]>
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