Sentence examples for mining approaches are from inspiring English sources

Most of the frequent subgraph mining approaches are unsupervised.

Whether using machine learning or statistical techniques, the text mining approaches are usually language independent.

Machine learning and data mining approaches are resourceful in determining interesting and non-trivial patterns from complex datasets.

Considering the methodological prospects discussed above, it will be crucial to perform sensitivity analyses if the results of text mining approaches are to be used for monitoring purposes.

Conventional graph mining approaches are best suited for binary edges, where the structure of graph objects is deterministic, and the binary edges represent the presence of linkages between the nodes [2].

In this paper, following three text mining approaches are proposed: (1) Extracting n-grams for text preprocessing and constructing domain ontology; (2) Extracting meaningful insights from text preprocessing; (3) Creating intuitive visual tools to understand the extracted insights.

In response to this, several data mining approaches were proposed to couple with this issue.

In the progress of this research various text mining approaches were attempted to identify relevant topics to characterise the publications.

In this study, scaling arguments and data mining approaches were used to derive formulae for the prediction of wave overtopping rate at fully vertical and nearly vertical impermeable solid smooth structures.

A common issue with rule mining approaches is the loss in performance from the statistical model itself; information can be lost during the extraction step and the resultant interpretable models are frequently not as accurate as the original model resulting in a trade-off between rule clarity and accuracy [8].

Such automated text mining approaches were not suitable for the curation of 3D domain swap-related features.