ENHANCED SLAP ALGORITHM TO FIND TOP FREQUENT ITEMSETS IN THE ONLINE RETAIL STORE
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Abstract
Online shopping plays an important role from the past few decades. E-commerce websites to increase their profits by performing business analytics on the top utility items. Existing transactional databases implemented sequential patterns to find top utilities. More number of resources and computations are required to access these databases. In the proposed research, the model recognizes the frequent patterns by extending the Genetic Approach known as “Slap”, a swarm optimization which finds the best solution for the frequent activities. Optimization technique in this research is associated with finding the top-k utilities with maximum accuracy. The major advantage of this approach lies in its colony formation in perfect shape by communicating with neighbours. The elements in the local search space continuously communicate with their agents and update their positions in global space. The implementation of genetic approaches with multi objective function helps the model to reduce the features and improve the accuracy. The proposed approach has obtained 97.1% accuracy.
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