Abstract
Background: This paper puts forward a parallel algorithm of association rules applicable for sales data analysis based on association rules by utilizing the idea of division and designs a sales management system for mall including behavior recognition and data analysis function as the application model of this algorithm with clothing store data management system as study object.
Objective: To adapt to the data particularity of the study object, while mining the association rules, the improved algorithm also considers the priority relations, weight, negative association rules, and other factors among different items of the database.
Methods: This improved algorithm is applied to Apriori algorithm, dividing the original database into n local data sets, mining the local data sets parallelly, finding out the local frequent data sets in each local data set, and finally counting the support and determine the final overall frequent sets.
Results: Experiment verifies that this algorithm reduces the visit times of the database, shortens the mining time of algorithm, and improves the effectiveness and adaptability of the mining result.
Conclusion: With the application with negative association rules added, data with diversified results can be mined during analyzing specific problems, mining efficiency is improved, the accuracy and adaptability of mining result is guaranteed, and the high efficiency of algorithm is also ensured. The improvement of increment mining efficiency of database will be considered next while the database is updated continuously.
Keywords: Association rules, apriori algorithm, partition, parallel mining, big data, frequent itemsets.
Graphical Abstract
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