A Modified Approach for Missing Values in Data Mining Based on Rough Set Theory, Divided –and-Conquer, Closest Fit Approach Idea
Keywords:
component, Data mining, missing data, Data preprocessing, Statistical methods, Prediction methods, Rough Set Theory, SeriallyAbstract
Missing data plays a key role in practical fields. How to remove this gap is the main objective of data preprocessing step in data-mining. Many methods such as Statistical and Prediction approaches are generally used for missing data analysis, but unfortunately both approaches have some disadvantages and applicable for serial missing values in column. This paper tries to remove these gaps which are resulting from the two mentioned methods. The proposed algorithm tries to merge up two previously mentioned methods. This modified approach utilizes the potential knowledge and laws suggested by the data in Information System, and some basic mathematical concepts and some concepts from Rough Set Theory. Experimental results show that the proposed algorithm provides better result than the above mentioned two methods.
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