A geometric graph is a labeled graph whose vertices are points in the 2D plane with an isomorphism invariant under geometric transformations such as translation, rotation, and scaling. While Kuramochi and Karypis (ICDM2002) extensively studied the frequent pattern mining problem for geometric subgraphs, the maximal graph mining has not been considered so far. In this paper, we study the maximal...

A geometric graph is a labeled graph whose vertices are points in the 2D plane with isomorphism invariant under geometric transformations such as translation, rotation, and scaling. While Kuramochi and Karypis (ICDM2002) extensively studied the frequent pattern mining problem for geometric subgraphs, the maximal graph mining has not been considered so far. In this paper, we study the maximal (o...

Graph Data mining has ushered into new era with advanced data mining techniques. Mining Frequent Sub Graphs is the crucial area which appeals the ease of extracting the patterns in the graph. Typical graph data like Social Networks, Biological Networks (for metabolic pathways) and Computer Networks needs analysis of virtual networks of a category. Such graphs need be modeled as layered to disti...

In recent years, data mining in graphs or graph mining have attracted much attention due to explosive growth in generating graph databases. The graph database is one type of database that consists of either a single large graph or a number of relatively small graphs. Some applications that produce graph database are as follows: Biological networks, semantic web and behavioral modeling. Among al...

Mining large graphs using distributed platforms has attracted a lot of research interests. Especially, large graph mining on Hadoop has been researched extensively, due to its simplicity and massive scalability. However, the design principle of Hadoop to maximize scalability often limits the efficiency of the graph algorithms. For this reason, the performance of graph mining algorithms running ...

In the paper we propose the algorithm which discovers both connected and unconnected frequent graphs from the graphs set. Our approach is based on depth first search candidate generation and direct execution of subgraph isomorphism test over database. Several search space pruning techniques are also proposed. Due to lack of unconnected graph mining algorithms we compare our algorithm with two g...