Gene Expression Datasets (GEDs) usually consist of the expression values of thousands of genes within hundreds of samples. Frequent itemset and association rule mining algorithms have been applied to discover significant co-expressions among multiple genes from GEDs. To perform these data analyses, gene expression values are commonly discretized into a predefined number of bins. Such an expert-driven and not trivial preprocessing step could bias the quality of the mining result. This paper presents a novel approach to discovering gene correlations from GEDs which does not require data discretization. By representing per-sample gene expression values as item weights, frequent weighted itemsets can be extracted. The discovery of weighted itemsets instead of traditional (not weighted) ones prevents experts from discretizing GEDs before analyzing them and thus improves the effectiveness of the knowledge discovery process. Experiments performed on real GEDs demonstrate the effectiveness of the proposed approach.
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