Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the geometry of similarity search: dimensionality curse and concentration of measure
Information Processing Letters
Fuzzy clustering with weighting of data variables
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - special issue on measures and aggregation: formal aspects and applications to clustering and decision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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Modern high-throughput technologies allow the systematic characterisation of an organism but provide excessive amounts of data such as results from microarray gene expression experiments. Combining the information from various experiments will help to expand the knowledge about an organism. However, the analysis of a data set comprising measurements for thousands of genes under many conditions, requires efficient techniques to be feasible at all. Here, we refine a frequent itemset mining approach for scanning a high-throughput data set in order to identify subsets of genes and subsets of conditions with similar data patterns. As a use case, screenings of 4699 mutant clones of Pseudomonas aeruginosa each with a disrupted gene were considered under 109 conditions. We found an unexpected gene group with highly overlapping phenotypes. Therefore our approach is suitable to simultaneously find objects with similar pattern in high-dimensional data sets and their key characteristics within reasonable time.