Research on pattern recognition of tourism consumer behavior based on fuzzy clustering analysis

Abstract Understanding customer behavior has become critical for marketers and tourist service providers since global travel has rapidly expanded. Recognizing consumer behavior patterns allows organizations to modify their strategy for better service delivery. However, the conventional approaches frequently fail to adequately capture the variety and complexity of tourism consumer behavior because of the large and diverse data available. This research seeks to investigate the use of fuzzy clustering analysis to better understand tourism consumer behavior patterns. The method combines fuzzy clustering algorithms with customer behavior data such as demographics, travel preferences, and purchasing patterns. The investigation reveals separate groups of consumers, providing insights into how various factors influence tourist purchasing decisions. The data were gathered using questionnaires, online booking platforms, and travel websites, where customers provided information about their previous travel experiences and preferences. Data preparation was used to normalize the data for analysis. Principal component analysis was employed to decrease dimensionality. The Sea Turtle Foraging Optimized Fuzzy C-Means clustering (STFO-FCMC) is presented as an extension of normal FCMC that incorporates an optimization procedure based on sea turtle foraging habits. This optimization enhances the accuracy and efficiency of cluster center selection and membership values, making STFO-FCMC especially well-suited for dealing with the complexity and unpredictability of tourism behavior data. The findings show multiple consumer behavior patterns, including diverse preferences for various types of tourist products and services, which are split by age, income, and travel objectives. The STFO-FCMC method is assessed using metrics, including accuracy of 97.84%, precision, recall, and F1-score. These data assist service providers create individualized services and marketing strategies that improve consumer satisfaction and business performance. Overall, fuzzy clustering analysis, particularly with the STFO-FCMC approach, is a successful tool for detecting tourist consumer behavior, with substantial promise for improving tourism product and service targeting.