Mining significant pairs of patterns from graph structures with class labels Export

@proceedings{citeulike:1560254, abstract = {In recent years, the problem of mining association rules over frequent itemsets in transactional data has been frequently studied and yielded several algorithms that can find association rules within a limited amount of time. Also more complex patterns have been considered such as ordered trees, unordered trees, or labeled graphs. Although some approaches can efficiently derive all frequent subgraphs from a massive dataset of graphs, a subgraph or subtree that is mathematically defined is not necessarily a better knowledge representation. We propose an efficient approach to discover significant rules to classify positive and negative graph examples by estimating a tight upper bound on the statistical metric. This approach abandons unimportant rules earlier in the computations, and thereby accelerates the overall performance. The performance has been evaluated using real world datasets, and the efficiency and effect of our approach has been confirmed with respect to the amount of data and the computation time.}, author = {Inokuchi, A. and Kashima, H.}, booktitle = {Data Mining, 2003. ICDM 2003. Third IEEE International Conference on}, citeulike-article-id = {1560254}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1250906}, journal = {Data Mining, 2003. ICDM 2003. Third IEEE International Conference on}, keywords = {freq\_sets}, pages = {83--90}, posted-at = {2007-08-14 14:00:42}, priority = {2}, title = {Mining significant pairs of patterns from graph structures with class labels}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1250906}, year = {2003} }

TY - CONF ID - citeulike:1560254 L3 - citeulike-article-id:1560254 N2 - In recent years, the problem of mining association rules over frequent itemsets in transactional data has been frequently studied and yielded several algorithms that can find association rules within a limited amount of time. Also more complex patterns have been considered such as ordered trees, unordered trees, or labeled graphs. Although some approaches can efficiently derive all frequent subgraphs from a massive dataset of graphs, a subgraph or subtree that is mathematically defined is not necessarily a better knowledge representation. We propose an efficient approach to discover significant rules to classify positive and negative graph examples by estimating a tight upper bound on the statistical metric. This approach abandons unimportant rules earlier in the computations, and thereby accelerates the overall performance. The performance has been evaluated using real world datasets, and the efficiency and effect of our approach has been confirmed with respect to the amount of data and the computation time. JF - Data Mining, 2003. ICDM 2003. Third IEEE International Conference on EP - 90 TI - Mining significant pairs of patterns from graph structures with class labels T2 - Data Mining, 2003. ICDM 2003. Third IEEE International Conference on SP - 83 KW - freq_sets AU - Inokuchi, A AU - Kashima, H PY - 2003/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1250906 ER -