Frequent approximate subgraphs as features for graph-based image classification
Knowledge-Based Systems
Approximate graph mining with label costs
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A new proposal for graph classification using frequent geometric subgraphs
Data & Knowledge Engineering
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In this paper, we present a novel graph database-mining method called APGM (APproximate Graph Mining) to mine useful patterns from noisy graph database. In our method, we designed a general framework for modeling noisy distribution using a probability matrix and devised an efficient algorithm to identify approximate matched frequent subgraphs. We have used APGM to both synthetic data set and real-world data sets on protein structure pattern identification and structure classification. Our experimental study demonstrates the efficiency and efficacy of the proposed method.