Existing graph mining algorithms typically assume that
the dataset can fit into main memory. As many large
graph datasets cannot satisfy this condition, truly scalable
graph mining remains a challenging computational
problem. In this paper, we present a new horizontal data
partitioning framework for graph mining. The original
dataset is divided into fragments, then each fragment is
mined individually and the results are combined together
to generate a global result. One of the challenging
problems in graph mining is about the completeness
because the of complexity graph structures. We will prove
the completeness of our algorithm in this paper. The
experiments will be conducted to illustrate the efficiency
of our data partitioning approach.
Cite as: Nguyen, S.N., Orlowska, M.E. and Li, X. (2008). Graph Mining based on a Data Partitioning Approach. In Proc. Nineteenth Australasian Database Conference (ADC 2008), Wollongong, NSW, Australia. CRPIT, 75. Fekete, A. and Lin, X., Eds. ACS. 31-37.
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