A Rough Set Approach for Generation and Validation of Rules for Missing Attribute Values of a Data Set

Data mining has emerged as most significant and continuously evolving field of research because of its ever growing and far reaching applications into various areas such as medical, military, financial markets, banking etc. One of the most useful applications of data mining is extracting significant and earlier unknown knowledge from real-world databases. This knowledge may be in the form of rules. Rule generation' from raw data is a very effective and most widely used tool of data mining. Real life data are frequently imperfect, erroneous, incomplete, uncertain and vague. There are so many approaches for handling missing attribute values.

Provided by: International Journal of Computer Applications Topic: Big Data Date Added: Mar 2012 Format: PDF

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