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Mining Weighted-Frequent Traversal Patterns using Graph Topology


Hyu Chan Park


Vol. 14  No. 10  pp. 20-26


Mining problem is to discover valuable patterns from large data set, such as item sets and graph traversals. This paper extends such mining problems to the case where vertices of graph are attached with weights to reflect their importance. Under such weight settings, traditional mining algorithms can not be adopted directly any more. To cope with the problem, this paper proposes new algorithms to discover weighted-frequent patterns from the traversals. Specifically, we devise support bound paradigms for candidate generation and pruning during the mining process


Data mining, Graph, Traversal, Weighted-frequent pattern