Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Generalizing the notion of support
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Topological Measurement for Weighted Protein Interaction Network
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Enhancing Data Analysis with Noise Removal
IEEE Transactions on Knowledge and Data Engineering
Data Mining and Knowledge Discovery
Prediction of Protein Function Using Common-Neighbors in Protein-Protein Interaction Networks
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Effective similarity measures for expression profiles
Bioinformatics
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Association Analysis Techniques for Bioinformatics Problems
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Mining High-Correlation Association Rules for Inferring Gene Regulation Networks
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Reliability study of mesh networks modeled as random graphs
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
Random spanning trees and the prediction ofweighted graphs
The Journal of Machine Learning Research
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Protein interaction networks are one of the most promising types of biological data for the discovery of functional modules and the prediction of individual protein functions. However, it is known that these networks are both incomplete and inaccurate, i.e., they have spurious edges and lackbiologically valid edges. One way to handle this problem is by transforming the original interaction graph into new graphs that remove spurious edges, add biologically valid ones, and assign reliability scores to the edges constituting the final network. We investigate currently existing methods, as well as propose a robust association analysis-based method for this task. This method is based on the concept of h-confidence, which is a measure that can be used to extract groups of objects having high similarity with each other. Experimental evaluation on several protein interaction data sets show that hyperclique-based transformations enhance the performance of standard function prediction algorithms significantly, and thus have merit.