TY  - JOUR
T1  - Fast Graph Isomorphism Testing for Graph Based Data Mining with Improved Canonical Labelling
AU - Kavitha, D. AU - Prasad, V. Kamakshi AU - Murthy, J.V.R. 
JO  - Journal of Engineering and Applied Sciences
VL  - 11
IS  - 7
SP  - 1586
EP  - 1597
PY  - 2016
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2016.1586.1597
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2016.1586.1597
KW  - Graph mining
KW  -graph isomorphism
KW  -canonical labelling
KW  -partition refinement
KW  -symmetry
AB  - In graph based data mining, graph/subgraph isomorphism testing used in mining frequent subgraphs plays key role and is time consuming. In a wide range of real applications, graph Isomorphism has significant role in retrieving the isomorphic graphs from a set of graphs. Canonical labelling of the graph has major impact on the efficiency of graph isomorphism testing. In this study, an algorithm is proposed to find canonical labelling in an efficient way and there by efficient isomorphism testing of labelled graphs. The proposed algorithm reduces search space based on the symmetries present in the graph there by making computation feasible to perform isomorphism testing on large databases for pattern mining.
ER  -