Evaluation of Tourist Satisfaction in Tourist Attractions Based on Data Mining with Improved Fuzzy C Means Clustering

In recent years, the tourism industry has experienced fast development and has become an important part of the global economy. This research aims to cluster travellers based on their social media interactions and determine various segments with similar or opposing preferences based on their travel choices. It also seeks to understand the behaviour of these traveller clusters in relation to destination selection, through an aim of designing tailored tour packages which improve tourist satisfaction as well as produce maintainable advancements. Thus, this research proposes the Improved Fuzzy K-means Clustering (IFCM) approach for the evaluation of tourist's satisfaction in tourist attractions in Data Mining. The proposed approach has demonstrated notable similarities in identifying the optimal number of clusters and analysing the behaviour of interested travellers' groups. This research identifies a “dynamic” of user-generated data over time and gain a better understanding of the aspects and concerns of visitor's satisfaction overtime.