  Data quality issues have attracted widespread attention due to the negative
impacts of dirty data on data mining and machine learning results. The
relationship between data quality and the accuracy of results could be applied
on the selection of the appropriate algorithm with the consideration of data
quality and the determination of the data share to clean. However, rare
research has focused on exploring such relationship. Motivated by this, this
paper conducts an experimental comparison for the effects of missing,
inconsistent and conflicting data on classification and clustering algorithms.
Based on the experimental findings, we provide guidelines for algorithm
selection and data cleaning.
