The success of tensor-based subspace learning depends heavily on reducing correlations along the column vectors of the mode-k flattened matrix. In this work, we study the problem ...
Shuicheng Yan, Dong Xu, Stephen Lin, Thomas S. Hua...
This paper presents an efficient algorithm for learning Bayesian belief networks from databases. The algorithm takes a database as input and constructs the belief network structur...
Frequent coherent subgraphscan provide valuable knowledgeabout the underlying internal structure of a graph database, and mining frequently occurring coherent subgraphs from large...
Zhiping Zeng, Jianyong Wang, Lizhu Zhou, George Ka...
Analysis of biological data often involves large data sets and computationally expensive algorithms. Databases of biological data continue to grow, leading to an increasing demand ...
Recent advances in data processing have enabled the generation of large and complex graphs. Many researchers have developed techniques to investigate informative structures within...