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Identifying Dense Clusters subgraph Large Networks. Recommended articles Citing articles 0. Knowledge and Information Systems. Bitcoins Biol 6 3: Mining significant graph patterns by leap search. Direct mining of discriminative and dense frequent patterns via model-based search tree.
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Visualizing and Mining Cohesive Subgraphs. The optimization of these three objectives usually is conflicting; thus, we realize a trade-off between these characteristics to obtain meaningful patterns. BMC Syst Biol 1 1: This service is more advanced with JavaScript available, learn more at http: Recommended articles Citing articles 0. Knowledge and Information Systems.
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Nucleic Bitcoins Res subgraph suppl 1: A fast and scalable tool for data mining in massive graphs. As for attribute data it is known that subgraph clustering often is futile, we have to analyze dense similarity of objects regarding subsets mining their attributes. An automated method for finding molecular complexes in large dense interaction networks. Moise G, Sander J Finding non-redundant, statistically significant mining in high dimensional data: An efficient algorithm bitcoins detecting frequent subgraphs in biological networks.
Dense subgraph mining with a mixed graph model - ScienceDirect
These properties enable us to employ an index-based method to locate all occurrences of a pattern in a graph and a depth-first search method to find all patterns. Concluding this work, a large number of real and synthetic data sets are used to show the effectiveness and efficiency of the DESSIN method.
Unable to display preview. This is a preview of subscription content, log in to check access. An automated method for finding molecular complexes in large protein interaction networks. What is Frequent in a Single Graph? International symposium on software testing and analysis Google Scholar. Finding frequent substructures in chemical compounds. Direct mining of discriminative and essential frequent patterns via model-based search tree.
Subgraph support in a single large graph. Mining Representative Orthogonal Graph Patterns. Mining coherent dense subgraphs across massive biological networks for functional discovery. Efficient mining of frequent subgraphs in the presence of isomorphism. An apriori-based algorithm for mining frequent substructures from graph data. An efficient algorithm for detecting frequent subgraphs in biological networks.
Identifying Dense Clusters in Large Networks. A quick start in frequent structure mining can make a difference. The basic premise is that many prevalent datasets consist of multiple types of information: Analyzing both information types simultaneously can increase the expressiveness of the resulting patterns.
Our patterns of interest are sets of objects that are densely connected within the associated graph and as well show high similarity regarding their attributes. As for attribute data it is known that full-space clustering often is futile, we have to analyze the similarity of objects regarding subsets of their attributes. In order to take full advantage of all present information, we combine the paradigms of dense subgraph mining and subspace clustering.
For our approach, we face several challenges to achieve a sound combination of the two paradigms. We maximize our twofold clusters according to their density, size, and number of relevant dimensions. The optimization of these three objectives usually is conflicting; thus, we realize a trade-off between these characteristics to obtain meaningful patterns.
We develop a redundancy model to confine the clustering to a manageable size by selecting only the most interesting clusters for the result set.
In thorough experiments on synthetic and real world data we show that GAMer achieves low runtimes and high clustering qualities. We provide all datasets, measures, executables, and parameter settings on our website http: Regular Paper First Online: This is a preview of subscription content, log in to check access. Lecture Notes in Computer Science pp. Aggarwal C, Wang H Managing and mining graph data. Random Struct Algorithms 18 2: Garey M, Johnson D Computers and intractability: SDM, pp — Google Scholar.
Han J, Kamber M Data mining: Jolliffe I Principal component analysis, 2nd edn.