TY - GEN
T1 - POSGRAMI
T2 - 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2016
AU - Moussaoui, Mohamed
AU - Zaghdoud, Montaceur
AU - Akaichi, Jalel
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - The frequent subgraph mining has widespread applications in many different domains such as social network analysis and bioinformatics. Generally, the frequent subgraph mining refers to graph matching. Many research works dealt with structural graph matching, but a little attention is paid to semantic matching when graph vertices and/or edges are attributed. Therefore, the discovered frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in the graph matching process. In this paper, we present POSGRAMI, a new hybrid approach for frequent subgraph mining based principally on approximate graph matching. To this end, POSGRAMI first uses an approximate structural similarity function based on graph edit distance function. POSGRAMI then uses a semantic vertices similarity function based on possibilistic information affinity function. In fact, our proposed approach is a new possibilistic version of existing approach in literature named GRAMI. This paper had shown the effectiveness of POSGRAMI on some real datasets. In particular, it achieved a better performance than GRAMI in terms of processing time, number and quality of discovered subgraphs.
AB - The frequent subgraph mining has widespread applications in many different domains such as social network analysis and bioinformatics. Generally, the frequent subgraph mining refers to graph matching. Many research works dealt with structural graph matching, but a little attention is paid to semantic matching when graph vertices and/or edges are attributed. Therefore, the discovered frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in the graph matching process. In this paper, we present POSGRAMI, a new hybrid approach for frequent subgraph mining based principally on approximate graph matching. To this end, POSGRAMI first uses an approximate structural similarity function based on graph edit distance function. POSGRAMI then uses a semantic vertices similarity function based on possibilistic information affinity function. In fact, our proposed approach is a new possibilistic version of existing approach in literature named GRAMI. This paper had shown the effectiveness of POSGRAMI on some real datasets. In particular, it achieved a better performance than GRAMI in terms of processing time, number and quality of discovered subgraphs.
KW - Approximate graph matching
KW - Frequent subgraph mining
KW - Graph mining
KW - Possibilistic similarity
KW - Possibility theory
UR - https://www.scopus.com/pages/publications/84977103323
U2 - 10.1007/978-3-319-40596-4_46
DO - 10.1007/978-3-319-40596-4_46
M3 - Conference contribution
AN - SCOPUS:84977103323
SN - 9783319405957
T3 - Communications in Computer and Information Science
SP - 549
EP - 561
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems - 16th International Conference, IPMU 2016, Proceedings
A2 - Vieira, Susana
A2 - Kaymak, Uzay
A2 - Carvalho, Joao Paulo
A2 - Lesot, Marie-Jeanne
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
PB - Springer Verlag
Y2 - 20 June 2016 through 24 June 2016
ER -