Frequent Sub-graph Mining on Edge Weighted Graphs

Frequent sub-graph mining entails two significant overheads The first is concerned with candidate set generation The second with isomorphism checking These are also issues with respect to other forms of frequent pattern mining but are exacerbated in the context of frequent sub-graph mining To reduced the search space and address these twin overheads 1 weighted approach to sub graph mining is proposed How ever a significant Issue in weighted sub-graph mining is that the anti monotone property typically used to control candidate set generation no longer holds This paper examines a number of edge weighting schemes and suggests three strategies for controlling candidate set generation The three strategies have been incorporated into weighted variations of gSpan ATW gSpan AW gSpan and UBW gSpan respectively A complete evaluation of all three approaches is presented