HybirdGMiner：The Mining Strategy on Frequent Isomorphism Graph Structure

• Main Authors: Cheng-Tao Ho, 何承道
• Other Authors: Chia-Hui Chang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/4n77ys

碩士 === 國立中央大學 === 資訊工程研究所 === 94 === As the mining of frequent itemsets and sequential patterns became more mature, it is very natural that we would want to explore other patterns such as graph structures. Graph mining has very wide applications, such as chemistry, biology and computer networks. The main challenge in graph mining is how to solve the graph/ subgraph isomorphism problems. Thus, we propose an algorithm that combined previous pattern mining skills and some graph mining techniques to mine all frequent subgraph patterns efficiently. Our algorithm adopts canonical form to avoid the duplicate enumeration, and used an effective embedding list structure to avert the subgraph isomorphism checking completely. Our empirical study on synthetic and real datasets demonstrates that HybridGMiner achieves a substantial performance gain over the algorithm gSpan.