Analysis of the Intelligent Tourism Route Planning Scheme Based on the Cluster Analysis Algorithm

Area of Science:

• Computer Science
• Data Mining
• Artificial Intelligence

Background:

• Traditional k-means clustering suffers from sensitivity to initial cluster centers.
• Existing tourism route planning methods lack personalization and efficiency.
• Addressing these limitations is crucial for advancing intelligent systems.

Purpose of the Study:

• To improve the traditional k-means cluster analysis algorithm.
• To apply the enhanced algorithm to intelligent tourism route planning.
• To develop a personalized and reasonable tourism route planning scheme.

Main Methods:

• Improvement of the k-means cluster analysis algorithm to overcome initial center dependency.
• Application of the improved k-means algorithm to intelligent tourism route planning.
• Integration of tourist preference metrics into the planning scheme.

Main Results:

• The improved k-means algorithm demonstrates a superior clustering effect compared to the traditional approach.
• The proposed intelligent tourism planning scheme shows practical reasonableness and provides valuable references.
• The method effectively incorporates user preferences into route generation.

Conclusions:

• The enhanced k-means algorithm offers a more robust clustering solution.
• The developed intelligent tourism route planning system is effective and user-centric.
• This research contributes to the field of intelligent systems and personalized travel planning.