Data quality issues such as missing values and outliers are often interdependent, which makes preprocessing both time-consuming and leads to suboptimal performance in knowledge discovery tasks. This package supports preprocessing decision making by visualizing interdependent data quality issues through means of feature construction. The user can define his own application domain specific constructed features that express the quality of a data point such as number of missing values in the point or use nine default features. The outcome can be explored with plot methods and the feature constructed data acquired with get methods.
|Author||Markus Vattulainen [aut, cre]|
|Date of publication||2016-07-09 10:10:07|
|Maintainer||Markus Vattulainen <[email protected]>|
|Package repository||View on CRAN|
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