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Mining Significant Patterns from Graph Traversals by Considering Frequency and Average Weight


Hyu Chan Park


Vol. 15  No. 11  pp. 15-20


Graph traversal is a sequence of vertices along edges on a graph, by which a lot of real world problems can be modeled. Mining patterns from such traversals has been found useful in several applications such as Web mining. However, previous works considered only frequency or summed weight of patterns. This paper extends them by considering average weight of patterns. Under such weight settings, traditional mining algorithms can not be adopted directly any more. To cope with the problem, this paper proposes new methodology by considering average weight along with frequency.


Data mining, Graph traversal, Average weight