Abstract: Aiming at the current puzzles of random resource allocation of cloud computing platform and lower mining efficiency of traditional induced subgraph,promoting the efficiency of resource integration and using of cloud computing platform and large-scale induced subgraph mining,the paper put forward an algorithm of large-scale induced subgraph extraction for self-adaption cloud to solve the problems of resource optimal utilization and massive graph mining．The paper firstly introduced the relevant concepts and problem description of cloud computing and induced subgraph mining,then designed an algorithm SAC_TA of self-adaption task dynamic allocation according to MapReduce parallel processing model,which can comput task self-adaption allocation system resources to reach the optimum of cost wasting,meanwhile designed the self-adaption cloud framework．On the basis of the framework,the paper put forward the massive induced subgraph mining algorithm SFGFF,which includs four stages of mining．And while applying all the algorithms to self-adaption cloud, the whole induced subgraph mining system can be constructed． The experimental result of manual simulation data and real environment data shows that the self-adaption cloud runs well and the algorithms are efficient and feasible,and have higher speed-up ratio and operating efficiency to satisfy the demand of massive frequent induced subgraph mining.

Key words: Big data,Data mining,Cloud computing,Induced subgraph,Subgraph isomorphism