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42. An Algorithm for Finding Frequent Patterns in a Large Sparse Graph.

Author

Xiao Feng Zhou, Lin Gao, and An Guo Dong

Subjects
DATA mining, GRAPHIC methods, MOLECULAR biology, TOPOLOGY, ELECTRIC circuits, EMBEDDINGS (Mathematics), ALGORITHMS
Abstract

the need for data mining on graph data sets has increased in the past few years. Frequent patterns play a critical role in many data mining. Overlap patterns are of central importance in postgenomic molecular biology and other fields. Finding frequent overlap patterns can read off biological function from the topology of biological network. Efficient algorithm for finding frequent patterns in a very large graph is the focus for this aim. This paper presents an algorithm that find the connected subgraphs that have a sufficient number of edge-overlap embeddings in a single large directed unlabeled sparse graph. We do experimental study on both real biological data and real electronic circuit data show that algorithm achieve good performance. [ABSTRACT FROM AUTHOR]

Published
2007

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