Rare Itemsets Selector with Association Rules for Revenue Analysis by Association Rare Itemset Rule Mining Approach

Background: Knowledge discovering rare itemsets mining, and association rules are significant in a transactional dataset. Be that as it may, rare association rules are now and again more intriguing than frequent association rules since rare rules indicate an unforeseen or obscure association. The utilization of rare itemsets mining with item selector is unavoidable and has turned into an emerging field of research; therefore, this subject has numerous challenges. Objective: To perform the revenue examination of the marketing sector by rare itemsets selector by threshold and time series-based prediction technique. Methods: This paper gives the revenue examination of the marketing sector by rare itemsets selector by threshold and time series-based prediction technique. A new algorithm is proposed for locating the rare itemsets by Association Rare itemset Rule Mining (ARIRM) to produce rules and then utility itemsets discovery by the threshold. When the rare patterns are analyzed, the ARIMA model is used to anticipate the revenue. Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items. Results: The test results in the "K" transactions with high revenues discovered utilizing the proposed model contrasted with other existing procedures; this forecast procedure is helpful for upcoming transactions. Conclusion: Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items.