Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining, indexing and similarity search in large graph data sets
Mining, indexing and similarity search in large graph data sets
Bioinformatics
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ChIP-chip and ChIP-seq are techniques for the isolation and identification of the binding sites of DNA-associated proteins along the genome. Both techniques produce genome-wide location data. The geometric arrangements of these binding sites can provide valuable information about biological function, such as the activation or repression of genes. In this paper, we formalize this problem and propose a novel graph based algorithm called Patterns of Marks (PoM) to discover efficiently these types of geometric patterns in genomic data. We also describe how we validate the algorithm using experimental data.