# Package org.deidentifier.arx.metric

Package providing access to quality models

See: Description

Class Summary
InformationLoss<T>: This class implements an abstract base class for information loss.
InformationLossDefaultWithBound: Information loss with a potential lower bound.
InformationLossWithBound<T extends InformationLoss<?>>: Information loss with a potential lower bound.
Metric<T extends InformationLoss<?>>: Abstract base class for metrics.
MetricAECS: This class provides an implementation of the (normalized) average equivalence class size metric.
MetricConfiguration: A class for a configuration of a metric.
MetricDefault: This class provides an abstract skeleton for the implementation of metrics.
MetricDescription: A class describing a metric and its configuration options.
MetricDM: This class provides an implementation of the DM metric (non-monotonic).
MetricDMStar: This class provides an implementation of the DM* metric (monotonic variant of the Discernability Metric).
MetricEntropy: This class provides an efficient implementation of the non-uniform entropy metric.
MetricHeight: This class provides an implementation of the Height metric.
MetricNMEntropy: This class provides an efficient implementation of a non-monotonic and non-uniform entropy metric.
MetricNMPrecision: This class provides an implementation of a weighted precision metric as proposed in: Sweeney, L. (2002).
MetricPrecision: This class provides an implementation of a monotonic weighted precision metric.
MetricStatic: This class provides an implementation of a static metric in which information loss is user-defined per generalization level.
MetricWeighted<T extends InformationLoss<?>>: This class provides an abstract skeleton for the implementation of weighted metrics.

Enum Summary
Metric.AggregateFunction: Pluggable aggregate functions.

## Package org.deidentifier.arx.metric Description

Package providing access to quality models

Author:
  Fabian Prasser, Florian Kohlmayer