cooccurenties

Cooccurenties, or co-occurrences, describe instances where two or more items appear within the same context, record, or time window. They are used to quantify how often items appear together compared with what would be expected by chance. Co-occurrence can be observed in text data, biological data, ecological surveys, and transactional records, among others.

Measurement and analysis: A co-occurrence matrix records counts of pairs of items. From such data, researchers compute similarity or association metrics, such as the Jaccard index, Dice coefficient, or pointwise mutual information (PMI). Lift and other statistical measures help assess whether co-occurrence is more frequent than expected under independence. Significance tests and null models (for example, permutation tests) are used to determine whether observed co-occurrences deviate from chance.

Applications: In natural language processing, word co-occurrence informs collocation detection and semantic similarity. In market basket analysis, products frequently bought together are identified for recommendations. In ecology, species co-occurrence patterns reveal potential interactions or shared habitat preferences. In epidemiology and medicine, comorbidity and co-prescription patterns are analyzed to understand disease associations.

Limitations: Co-occurrence does not establish causation and can be influenced by data collection biases, dataset size, and common causes. High-frequency items may inflate co-occurrence counts. Higher-order co-occurrences (three or more items) are more complex to model and interpret than pairwise cases.

Representation: Co-occurrence networks or graphs model items as nodes with edges weighted by co-occurrence strength or frequency, enabling visualization and network analysis.