Treffer: Classification of advanced data mining techniques for high-density data management in E-commerce via CHF Frank power approach.

The explosive growth of high-density data in e-commerce environments presents significant challenges in selecting and deploying suitable data mining techniques. This selection process is inherently a multi-criteria decision-making (MCDM) problem, as it involves evaluating multiple, often conflicting, criteria such as scalability, accuracy, real-time processing, and robustness under uncertainty. To address this complexity, this study proposes a novel classification framework based on the complex hesitant fuzzy Frank power (CHFFP) aggregation approach. The use of complex hesitant fuzzy sets (CHFSs) is motivated by the need to model hesitation and complex-valued information simultaneously, which is often encountered in high-volume transactional data. Traditional fuzzy set models lack the expressive power to capture such multi-dimensional uncertainty effectively. By integrating the Frank power aggregation operator, this approach introduces a parameterized and adaptable decision-making mechanism suited for nuanced e-commerce scenarios. The proposed CHFFP model is applied to classify and prioritize advanced data mining techniques across multiple evaluation criteria. The results confirm that this MCDM-based classification approach improves the decision-making process by offering a more accurate and context-aware ranking of alternatives, thereby supporting the efficient management of high-density data in e-commerce platforms. Moreover, the main findings are; Complex hesitant fuzzy Frank power averaging (CHFFPA) operator. Complex hesitant fuzzy Frank power weighted averaging (CHFFPWA) operator. Complex hesitant fuzzy Frank power geometric (CHFFPG) operator. Complex hesitant fuzzy Frank power weighted geometric (CHFFPWG) operator. Case Study and related mathematical interpretations for the best classification. Comparative and sensitivity analysis to show the significance and stability of the proposed methods. [ABSTRACT FROM AUTHOR]

Copyright of Evolutionary Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)