FS 3 : A sampling based method for top-k frequent subgraph mining
作者： Tanay Kumar Saha , Mohammad Al Hasan
DOI: 10.1109/BIGDATA.2014.7004359
关键词:
摘要: Mining labeled subgraph is a popular research task in data mining because of its potential application many different scientific domains. All the existing methods for this explicitly or implicitly solve isomorphism which computationally expensive, so they suffer from lack scalability problem when graphs input database are large. In work, we propose FS3, sampling based method. It mines small collection subgraphs that most frequent probabilistic sense. FS3 performs Markov Chain Monte Carlo (MCMC) over space fixed-size such potentially sampled more often. Besides, equipped with an innovative queue manager. stores finite course manner top-k positions contain subgraphs. Our experiments on large show efficient, and it obtains amongst given size.
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