On Computing All Maximal Cliques Distributedly
IRREGULAR '97 Proceedings of the 4th International Symposium on Solving Irregularly Structured Problems in Parallel
Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
A New Conceptual Clustering Framework
Machine Learning
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Distributed Algorithm to Enumerate All Maximal Cliques in MapReduce
FCST '09 Proceedings of the 2009 Fourth International Conference on Frontier of Computer Science and Technology
X-RIME: Cloud-Based Large Scale Social Network Analysis
SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
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We propose a novel data mining framework using relaxed biclique for heterogeneous data. The framework is composed of three algorithms. First, an enumeration algorithm transforms heterogeneous databases into relaxed bicliques. Second, a tracking algorithm is used to find the bicliqueâ聙聶s variations over time. Finally, a ranking algorithm classifies relaxed bicliques into groups according to their statistical properties and dynamic behaviors. The framework is highly flexible and can be easily extended to applications in different domains. The framework is implemented in MapReduce and is proven to be scalable for processing large-scale data in a reasonable amount of time. In addition, the experiments show that the algorithms are both scalable and efficient. The proposed framework can also be applied to web network analysis and deliver rapid-response solutions.