Dealing with Missing Values in Data

Dealing with Missing Values in Data

Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Problems associated with missing values are loss of efficiency, complications in handling and analyzing the data and bias resulting from differences between missing and complete data. The important factor for selection ...

Dealing with Missing Values in the Data Warehouse

Dealing with Missing Data

Rough sets methodology is a useful tool for analysis of decision problems concerning a set of objects described in a data table by a set of condition attributes and by a set of decision attributes. In practical applications, however, the data table is often not complete because some data are missing. To deal with this case, we propose an extension of the rough set methodology to the analysis of...

Techniques for Dealing with Missing Values

A brief overview of the history of the development of decision tree induction algorithms is followed by a review of techniques for dealing with missing attribute values in the operation of these methods. The technique of dynamic path generation is described in the context of tree-based classiication methods. The waste of data which can result from casewise deletion of missing values in statisti...

Combined association rules for dealing with missing values

With the rapid increase in the use of databases, the problem of missing values inevitably arises. The techniques developed to effectively recover these missing values should be highly precise in order to estimate the missing values completely. The mining of association rules can effectively establish the relationship among items in databases. Therefore, discovered association rules are usually ...