To our best knowledge, all existing graph pattern mining algorithms can only mine either closed, maximal or the complete set of frequent subgraphs instead of graph generators whic...
Zhiping Zeng, Jianyong Wang, Jun Zhang, Lizhu Zhou
Graphs capture the essential elements of many problems broadly defined as searching or categorizing. With the rapid increase of data volumes from sensors, many application discipl...
Mining frequent patterns in transaction databases has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithms do not con...
Extracting dense sub-components from graphs efficiently is an important objective in a wide range of application domains ranging from social network analysis to biological network...
Nan Wang, Srinivasan Parthasarathy, Kian-Lee Tan, ...
Pattern mining methods for graph data have largely been restricted to ground features, such as frequent or correlated subgraphs. Kazius et al. have demonstrated the use of elaborat...
Andreas Maunz, Christoph Helma, Tobias Cramer, Ste...