Sentence examples for extract association from inspiring English sources

Association rules mining (ARM) Based on the comparative study [15], we used the FP-growth algorithm to extract association rules adapted (PFP-growth) to the context of big data.

To extract association rules, two measures are required: the support and the confidence.

Similarly to an FP-tree, this structure allows to compactly store all the information needed to extract association rules reading the dataset only twice.

In the literature, several algorithms have been proposed to extract association rules, among them we find Apriori, the key algorithm proposed by Agrawal to extract the frequent itemset.

The MR-agent receives a dataset and chooses the appropriate algorithm to extract association rules after receiving the minimum support from the control agent and sends the result to the knowledge base to be used by the principal agent.

This approach consists of two major steps; a rules generator using the Apriori algorithm to extract association rules, and multi-criteria decision analysis to evaluate and select the interesting rules from the large set extracted.

Simple metrics are used to extract associations between the cluster units and the input vectors assigned to them.

In addition to integrating manually curated annotations, experimental data and predictions, we use automatic text mining to extract associations from the biomedical literature.

In De la Fuente et al. (2004), the authors used Partial Pearson's correlation to extract associations between pairs of genes when this association can be explained by means of a third gene.

Other approaches mostly use direct (rather than semantic) matching (e.g., BLAST searches, homology mapping, shared GO annotations, etc)., or use co-occurrence-based and/or rule-based approaches to extract associations from PudMed abstracts (e.g., STRING searches for recurrent co-mentioning of gene names; PPI-Finder mines protein interactions based on their co-occurrences and interaction words).

It is often the case that the dimensionality of large-scale biological data (in our case, number of COGs) is much larger than the number of samples (genomes), and the NETCAR algorithm may be appropriate to extract associations from other such data types.