Fuzzy, crisp, and human logic in e-commerce marketing data mining

Kelda L. Hearn; Yanqing Zhang

doi:10.1117/12.421093

27 March 2001 Fuzzy, crisp, and human logic in e-commerce marketing data mining

Kelda L. Hearn, Yanqing Zhang

Proceedings Volume 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III; (2001) https://doi.org/10.1117/12.421093
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

In today's business world there is an abundance of available data and a great need to make good use of it. Many businesses would benefit from examining customer habits and trends and making marketing and product decisions based on that analysis. However, the process of manually examining data and making sound decisions based on that data is time consuming and often impractical. Intelligent systems that can make judgments similar to human judgments are sorely needed. Thus, systems based on fuzzy logic present themselves as an option to be seriously considered. The work described in this paper attempts to make an initial comparison between fuzzy logic and more traditional hard or crisp logic to see which would make a better substitute for human intervention. In this particular case study, customers are classified into categories that indicate how desirable the customer would be as a prospect for marketing. This classification is based on a small set of customer data. The results from these investigations make it clear that fuzzy logic is more able to think for itself and make decisions that more closely match human decision and is therefore significantly closer to human logic than crisp logic.

Citation Download Citation

Kelda L. Hearn and Yanqing Zhang "Fuzzy, crisp, and human logic in e-commerce marketing data mining", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); https://doi.org/10.1117/12.421093

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