Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
Geometric and combinatorial tiles in 0-1 data
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Succinct summarization of transactional databases: an overlapped hyperrectangle scheme
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
From disease ontology to disease-ontology lite
Bioinformatics
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Matrix completion from a few entries
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Block interaction: a generative summarization scheme for frequent patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Journal of Biomedical Informatics
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Binary (0,1) matrices, commonly known as transactional databases, can represent many application data, including gene-phenotype data where "1” represents a confirmed gene-phenotype relation and "0” represents an unknown relation. It is natural to ask what information is hidden behind these "0”s and "1”s. Unfortunately, recent matrix completion methods, though very effective in many cases, are less likely to infer something interesting from these (0,1)-matrices. To answer this challenge, we propose Ind Evi, a very succinct and effective algorithm to perform independent-evidence-based transactional database transformation. Each entry of a (0,1)-matrix is evaluated by "independent evidence” (maximal supporting patterns) extracted from the whole matrix for this entry. The value of an entry, regardless of its value as 0 or 1, has completely no effect for its independent evidence. The experiment on a gene-phenotype database shows that our method is highly promising in ranking candidate genes and predicting unknown disease genes.