Extensible Framework for Data Pattern Exploration
Add additional interest measures for association rules
Create an association matrix from a nugget of flavour associations.
Bound a range of numeric values
Cluster association rules
Search for association rules
Search for conditions that yield in statistically significant one-samp...
Search for conditions that provide significant differences in selected...
Search for conditional correlations
Search for grid-based rules
Search for conditions that provide significant differences between pai...
Find tautologies or "almost tautologies" in a dataset
Search for patterns of a custom type
Show interactive application to explore association rules
Obtain truth-degrees of conditions
Format a vector of predicates into a condition string
Geom for drawing diamond plots of lattice structures
Test whether a vector is almost constant
Check whether a list of character vectors contains valid conditions
Test whether an object contains numeric values from the interval $[0,1...
Test whether an object is a nugget
Determine whether one vector is a subset of another
Create a nugget object of a given flavour
nuggets: Extensible Framework for Data Pattern Exploration
Convert condition strings into lists of predicate vectors
Convert columns of a data frame to Boolean or fuzzy sets (triangular, ...
Objects exported from other packages
Remove almost constant columns from a data frame
Remove invalid conditions from a list
Shorten predicates within conditions
Extract values from predicate names
Create a tibble of combinations of selected column names
Extract variable names from predicate names
Return indices of first elements of the list, which are incomparable w...
A framework for systematic exploration of association rules (Agrawal et al., 1994, <https://www.vldb.org/conf/1994/P487.PDF>), contrast patterns (Chen, 2022, <doi:10.48550/arXiv.2209.13556>), emerging patterns (Dong et al., 1999, <doi:10.1145/312129.312191>), subgroup discovery (Atzmueller, 2015, <doi:10.1002/widm.1144>), and conditional correlations (Hájek, 1978, <doi:10.1007/978-3-642-66943-9>). User-defined functions may also be supplied to guide custom pattern searches. Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions expressed as elementary conjunctions, evaluates them on a dataset, and inspects the induced sub-data for statistical, logical, or structural properties such as associations, correlations, or contrasts. Includes methods for visualization of logical structures and supports interactive exploration through integrated Shiny applications.
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