An Intra-algorithm Comparison Study of Complete Search FSM Implementations in Centralized Graph Transaction Databases

Abstract

Frequent subgraph mining (FSM) algorithms are widely used in various areas of data analysis. Several experimental studies about FSM algorithms were reported in literature; however, these experiments lack some clarifications about the most efficient implementation of a specific algorithm for a context of use (e.g., medium size datasets). In this paper, we present an experimental study with available implementations of two well known complete search FSM algorithms namely gSpan and Gaston. Our main purpose of this experimental study is to find a suitable Frequent Subgraph Mining implementation for indexing centralized graphs databases for aggregated search(CAIR home page: www.irit.fr/CAIR). In this paper, we provide details of the experimental results according to the input variation cases. We propose (for end users) a summary, about the most efficient FSM implementations for each algorithm (i.e., gSpan and Gaston), based on real datasets from the literature.