Mining graph databases for frequent subgraphs has recently developed into an area of intensive research. Its main goals are to reduce the execution time of the existing basic algorithms and to enhance their capability to find meaningful graph fragments. Here we present a method to achieve the former, namely an improvement of what we called "perfect extension pruning'' in an earlier paper. With this method the number of generated fragments and visited search tree nodes can be reduced, often considerably, thus accelerating the search. We describe the method in detail and present experimental results that demonstrate its usefulness
The output of frequent pattern mining is a huge number of frequent patterns, which are very redundan...
To our best knowledge, all existing graph pattern mining al-gorithms can only mine either closed, ma...
Due to rapid development of the Internet technology and new scientific advances, the number of appli...
Mining graph databases for frequent subgraphs has recently developed into an area of intensive resea...
In recent years several algorithms for mining frequent subgraphs in graph databases have been propos...
The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challen...
In many recent applications, a graph is used to simulate many complex systems, such as social networ...
AbstractFrequent subgraph mining (FSM) is defined as finding all the subgraphs in a given graph that...
Graphs are often used as models in very different application areas ranging from networks to molecul...
Abstract. Frequent approximate subgraph (FAS) mining has become an interesting task with wide applic...
AbstractGiven a database of graphs, structure mining algorithms search for all substructures that sa...
Abstract. In the past few years many algprilluns for 4iscovering frequent subgraphs in graph databas...
Graph data has been so prevalent that efficiently obtaining useful information from them is highly d...
Abstract. The main practical problem encountered with frequent subgraph search methods is the tens o...
With the increasing prevalence of data that model relationships between various entities, the use of...
The output of frequent pattern mining is a huge number of frequent patterns, which are very redundan...
To our best knowledge, all existing graph pattern mining al-gorithms can only mine either closed, ma...
Due to rapid development of the Internet technology and new scientific advances, the number of appli...
Mining graph databases for frequent subgraphs has recently developed into an area of intensive resea...
In recent years several algorithms for mining frequent subgraphs in graph databases have been propos...
The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challen...
In many recent applications, a graph is used to simulate many complex systems, such as social networ...
AbstractFrequent subgraph mining (FSM) is defined as finding all the subgraphs in a given graph that...
Graphs are often used as models in very different application areas ranging from networks to molecul...
Abstract. Frequent approximate subgraph (FAS) mining has become an interesting task with wide applic...
AbstractGiven a database of graphs, structure mining algorithms search for all substructures that sa...
Abstract. In the past few years many algprilluns for 4iscovering frequent subgraphs in graph databas...
Graph data has been so prevalent that efficiently obtaining useful information from them is highly d...
Abstract. The main practical problem encountered with frequent subgraph search methods is the tens o...
With the increasing prevalence of data that model relationships between various entities, the use of...
The output of frequent pattern mining is a huge number of frequent patterns, which are very redundan...
To our best knowledge, all existing graph pattern mining al-gorithms can only mine either closed, ma...
Due to rapid development of the Internet technology and new scientific advances, the number of appli...