Efficient evolutionary computation model of closed high-utility itemset mining

Jerry Chun-Wei Lin, Youcef Djenouri, Gautam Srivastava, Philippe Fournier-Viger

Abstract: HUIM has been an important issue in recent years, particularly in basket-market analysis, since it identifies useful information or goods for decision-making. Numerous research focused on extracting high-utility itemsets from datasets, revealing a tremendous amount of pattern information. This approach is incapable of providing correct choices in a short amount of time, e.g., real-time and online decision-making systems since it is difficult to extract relevant and important information in a short period of time from a huge body of found knowledge. Discovering closed patterns with high utilization (or closed pattern mining with high utility) is a market engineering method that discovers fewer but lucrative patterns. However, prior research has been unable to handle huge data, which is incompatible with today’s Internet of Things (IoT) environments, where huge volumes of data are collected every second. We begin by introducing the multi-objective model for mining the closed high utility itemsets (called MCUI-Miner), which employs MapReduce frameworks of a Spark structure. To begin, the multi-objective k-means the model is used to categorize transactions based on their significant relationship to the frequency component. The MapReduce model and GA are used to examine potential and probable candidates for mining closed high-utility itemsets in a large-scale database. Experiments have shown that the proposed framework outperforms the conventional CLS-Miner in terms of runtime, memory use, and scalability.