FSG is a apriori-based and therefore uses level-wise algorithm
Faces two challenges:
candidate generation: the generation of size subgraph candidates is more complicated and costly
pruning false positives: subgraph isomorphism test is an NP-complete problem
Uses Depth-First-Search (DFS)
can be used to find frequent sub graphs one by one from small to large ones.
No candidate generation and false test
Better saving of space by DFS.
Pattern growth mathod
GRAPH DATASET FREQUENT PATTERNS (MIN SUPPORT IS 2) (A) (B) (C) (1) (2)
Another three approaches to mine graph based data.
Inductive Logic Programming approach
Inductive database approach
Kernel function based approach
ILP approach. ILP systems constructs predictive model for a given data set by searching large space of candidate hypothesis.
WARMR – proposed in 1998. Combination of Apriori-like level wise search and IPL method.
But have a high computational complexity.
FARMER – proposed in 2011. Runs two orders of magnitude than WARMER.
Inductive DB approach. Databases which are capable of handling patterns within data. Quite different from from typical data bases. Uses interactive querying process to mine data in these data bases.
MolFea is an effort related to this area. Has a better computational efficiency which mines linear fragments in chemical compounds..
Also this performs a complete search of the paths in graph data.
Kernel Function based approach This “kernel” function basically defines similarity between two graphs The paper consists of two efforts done based on this approach, which classifies the graphs in to binary classes by SVM (Support Vector - Machine).