Rare Itemsets Selector with Association Rules for Revenue Analysis by Association Rare Itemset Rule Mining Approach
S. Selvarani, M. Jeyakarthic · Recent Advances in Computer Science and Communications · 2020
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 marketing sector by rare itemsets selector by threshold and time series-based prediction technique. A new algorithm for locating the rare itemsets by Association Rare itemset Rule Mining (ARIRM) to produced rules then utility itemsets discover by the threshold. When the rare patterns are analyzed, the ARIMA model 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.