Name
Abstract | ||
|---|---|---|
The existing methods for graph-based data mining (GBDM) follow the basic approach of applying a single-objective search with a user-defined threshold to discover interesting subgraphs. This obliges the user to deal with simple thresholds and impedes her/him from evaluating the mined subgraphs by defining different ''goodness'' (i.e., multiobjective) criteria regarding the characteristics of the subgraphs. In previous papers, we defined a multiobjective GBDM framework to perform bi-objective graph mining in terms of subgraph support and size maximization. Two different search methods were considered with this aim, a multiobjective beam search and a multiobjective evolutionary programming (MOEP). In this contribution, we extend the latter formulation to a three-objective framework by incorporating another classical graph mining objective, the subgraph diameter. The proposed MOEP method for multiobjective GBDM is tested on five synthetic and real-world datasets and its performance is compared against single and multiobjective subgraph mining approaches based on the classical Subdue technique in GBDM. The results highlight the application of multiobjective subgraph mining allows us to discover more diversified subgraphs in the objective space. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.jcss.2013.03.005 | J. Comput. Syst. Sci. |
Keywords | Field | DocType |
interesting subgraphs,multiobjective gbdm,three-objective subgraph mining,classical graph mining objective,diversified subgraphs,multiobjective subgraph mining,graph-based data mining,multiobjective gbdm framework,multiobjective evolutionary programming,bi-objective graph mining,multiobjective beam search,multiobjective optimization | Graph,Mathematical optimization,Beam search,Multi-objective optimization,Evolutionary programming,Maximization,Mathematics | Journal |
Volume | Issue | ISSN |
80 | 1 | 0022-0000 |
Citations | PageRank | References |
8 | 0.52 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prakash Shelokar | 1 | 38 | 3.33 |
| Arnaud Quirin | 2 | 168 | 13.68 |
| íscar Cordón | 3 | 82 | 4.34 |