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Classification Rule-Mining to Portend the Missing Items


Affiliations
1 Department of CSE, Avinashilingam University for Women, Coimbatore, India
2 Department of CSE, Anna University of Technology, Coimbatore, India
     

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This paper deals with the shopping cart in which an item can be chosen in advance and made available for the customer for buying an item which he/she is going to buy. It describes about the market basket in which the similar items are grouped together and they lead a group. It uses the Association Rule in which the common items are held in a group and provides a chance for the customer to choose one among them. This can be done with the help of certain percentage amount of buying the same thing by the regular customer. It uses a specialized algorithm for providing the association methodology. A commonly-used and naive solution to process data with missing attribute values is to ignore the instances which contain missing attribute values. This method may neglect important information within the data and a significant amount of data could be easily discarded. Some methods, such as assigning the most common values or assigning an average value to the missing attribute, make good use of all the available data. However the assigned value may not come from the information which the data originally derived from, thus noise is brought to the data.

Keywords

Association Rule Mining (ARM),classification Rule Mining.
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  • Classification Rule-Mining to Portend the Missing Items

Abstract Views: 280  |  PDF Views: 3

Authors

S. Sivaranjani
Department of CSE, Avinashilingam University for Women, Coimbatore, India
D. Palanikkumar
Department of CSE, Anna University of Technology, Coimbatore, India

Abstract


This paper deals with the shopping cart in which an item can be chosen in advance and made available for the customer for buying an item which he/she is going to buy. It describes about the market basket in which the similar items are grouped together and they lead a group. It uses the Association Rule in which the common items are held in a group and provides a chance for the customer to choose one among them. This can be done with the help of certain percentage amount of buying the same thing by the regular customer. It uses a specialized algorithm for providing the association methodology. A commonly-used and naive solution to process data with missing attribute values is to ignore the instances which contain missing attribute values. This method may neglect important information within the data and a significant amount of data could be easily discarded. Some methods, such as assigning the most common values or assigning an average value to the missing attribute, make good use of all the available data. However the assigned value may not come from the information which the data originally derived from, thus noise is brought to the data.

Keywords


Association Rule Mining (ARM),classification Rule Mining.