Analysis of Frequent Item Set in Super Market by using Enhanced FP-Growth Algorithm |
Author(s): |
| D. Jyosthna , KMM institute of post graduates studies; Ms. S. Anthony Mariya Kumari, KMM institute of post graduates studies |
Keywords: |
| Data Mining, Supermarket Analysis, Frequent Patterns, Association Rule Mining, FP-growth Algorithm |
Abstract |
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Association Rules, however the rules itself remains the market analysis is a process to analyze the buyer’s habits to get the correlations between the different items in their shopping cart. Calculated correlations can help the retailers to ascertain a profitable sales strategy by considering frequently purchased items together by customers. Association rule mining is one of the famous data mining techniques used to discover the correlations between one items to another. Association rule mining technique has range of algorithms, but this focuses on the effectiveness. The combination of the three association rule mining algorithms are "FP-GROWTH algorithm". Used to frequent Item sets in supermarket analysis. The collaboration of all algorithms revealed that three ways use the same concept with different criteria of processing the same. |
Other Details |
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Paper ID: IJSRDV7I10816 Published in: Volume : 7, Issue : 1 Publication Date: 01/04/2019 Page(s): 1127-1130 |
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