Abstract
Utility mining is one of the most thriving research topics with a wide range of real-world applications. High utility pattern mining uses a utility function to extract all desired patterns that exceed a minimum utility threshold. However, a significant number of patterns will be generated if this threshold is set too low, which is an inherent limitation of these algorithms. This may cause the mining process to be inefficient as it would be difficult to analyze the patterns found. Furthermore, most of these patterns are unreliable and hard to be employed in making decisions. This paper proposed a novel problem of mining reliable high utility patterns by adapting the concept of reliability to mine a significant type of pattern called reliable high utility patterns. To address this issue, an efficient approach named RUPM (Reliable Utility-based Pattern Mining) is presented. RUPM introduces three novel measurements for estimating the reliability of utility-based patterns and proposes several strategies to efficiently handle reliable patterns with high utility values. Experimental results suggest that up to 99% of the patterns discovered by existing traditional high utility pattern mining algorithms were, in fact, unreliable. In contrast, the average reliability proportion in the resultant patterns obtained from the RUPM approach is at least 47.6% higher. Moreover, the proposed pruning strategies provide a reduction in both the runtime and memory usage.
Highlights
Frequent Patterns Mining (FPM) is an analytical process that uses a co-occurrence measurement as a sole criterion to extract valuable patterns from a transaction database [1]
High Utility Itemset Mining (HUIM) refers to extracting all itemsets that exceed a predefined minimum utility threshold minUtil set by the user using a utility function [4], [5]
This diagram is divided into two parts: the first part includes the first six processes, which are responsible for generating potential reliable high utility patterns by implementing the proposed pruning strategies, whereas the second part includes the last three processes, FIGURE 1
Summary
Frequent Patterns Mining (FPM) is an analytical process that uses a co-occurrence measurement as a sole criterion to extract valuable patterns (i.e., itemset, sequence, rule, etc.) from a transaction database [1] This process aims to discover valuable associations between items in transactions, which are employed in numerous real-world applications in different domains [2]. Reliability mining is essential for financial markets, predicting product demand, and the retail industry because it analyses consumption behaviors in buying the products and predicting interesting products in the future [10] It is a critical research problem to design an algorithm for mining itemsets that have reliable behavior and generate a high profit.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
