Abstract
High-utility based itemset mining is the advancement of recurrent pattern mining that discovers occurrence of frequent transactions from a huge database. The issues in frequent pattern mining involve the elimination of quantities purchased by the customers and cost of purchased product. This can be resolved by high utility itemset mining which includes quantities and profit of the products in the transactions. The conventional association rule mining algorithms results in huge memory consumption due to the complexity in pruning the search space. In this paper, machine learning based high-utility itemset mining is applied to predict next order in an online grocery store depending on the transactions. The overall goal is to enhance the business profitability by stocking the high utility items in market. The Top ‘N’ variant Random Forest model is proposed to recommend the high utility itemsets, thereby predicting the reordered/next ordered items. The model is evaluated using Instacart market dataset to measure accuracy, precision and recall.
Highlights
Decision making is the significant part of a profitable business strategy that gains insight from the real time transactional databases
Association Rule Mining (ARM) that helps in identifying the frequently accessed itemsets from the database is termed as Frequent Itemset Mining (FRM) [3]
Apart from the conventional way of association rule mining, the machine learning based high utility itemset mining plays a significant role in decision making
Summary
Decision making is the significant part of a profitable business strategy that gains insight from the real time transactional databases. Transactional status of customers is utilized to recognize the purchase pattern and to improve the business profitability [2]. Apart from the conventional way of association rule mining, the machine learning based high utility itemset mining plays a significant role in decision making. The candidate key generation and rule based frequent itemsets are integrated in the classification-based association rule mining. The interesting patterns can be mined from the transactions given in the itemset. In the process of framing classification rules, the data items are represented as X and the class label is termed as C. The classification rules are generated similar to the association rules based on the traditional apriori algorithm such as XX → CC, where X is the dataset and C is the target class.
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