Applying Ordinal Association Rules for Cleansing Data With Missing Values
نویسندگان
چکیده
Cleansing data of errors is an important processing step particularly when integrating heterogeneous data sources. Dirty data files are prevalent in data warehouses because of incorrect or missing data values, inconsistent attribute naming conventions or incomplete information. This paper improves the data cleansing ordinal association rules technique by proposing a solution for the missing values problem. The approximated values for missing data items can be incorporated in the ordinal association rules. Experimental results confirm the effectiveness of the proposed enhancement.[Journal of American Science 2009:5(3) 52-62] ( ISSN: 1545-1003) Key word: Ordinal Association rules; Data cleansing; missing values; data mining.
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