Improvised Swarm Based Discrete Data Mining Approach for High Utility Item Sets
Keywords:
High Utility Itemset, discrete, Improved discrete cuckoo searchAbstract
Data mining techniques uncover valuable patterns hidden inside extensive databases to assist decision support systems in different practical applications. Association rule mining analyzes the transaction database to recognize patterns and provides insights into client behavior. Frequent itemset mining (FIM) detects a group of items that are commonly purchased together. A significant limitation of FIM is its disregard for the item's significance. The significance of an item is crucial in a practical application. Hence, it is imperative to identify the critical set of items that yields substantial profits, referred to as the HUIM (High-Utility Itemset Mining) problem. Various techniques may be employed to identify high utility itemsets from a transaction database. The HUIM approaches that employ the Utility list are relatively new and exhibit superior performance in terms of memory consumption and execution time. Primary constraint of these algorithms is the execution of expensive utility list join operations. This work presents a highly efficient optimization approach based on swarm intelligence for addressing the HUIM problem. Furthermore, the suggested method's execution time is assessed. Additionally, it is compared to relevant and advanced current approaches. Extensive tests were carried out on accessible benchmark datasets demonstrate that the suggested swarm-based methodology outperforms state-of-the-art approaches.
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