Improvement of K-Means Algorithm and Its Application in Air Passenger Grouping

Abstract

The k-means is one of the most popular clustering analysis algorithm and widely used in various fields. Nevertheless, it continues to have some shortcomings, for example, extremely sensitive to the initial center points selection and the special points such as noise or outliers. Therefore, this paper proposed initial center points’ selection optimization and phased assignment optimization to improve the k-means algorithm. The experimental results on 15 real-world and 10 synthetic datasets show that the improved k-means outperforms its main competitor k-means + + and under the same setting conditions, namely, using the default parameters,its clustering performance is better than Affinity Propagation, Mean Shift, and DBSCAN. The proposed algorithm was applied to analyze the airline seat selection data to air passengers grouping. The clustering results, as well as absolute deviation rate analysis, realized customer grouping and found out suitable audience group for the recommendation of seat selection services.

Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset

“…The K-means cluster is a more powerful method when dealing with massive data, which is becoming popularly used in big data-driven transportation pattern classification studies. We can see the typical applications in air passenger grouping [47], tourist pattern grouping [48], or travel purpose classification [49] based on traffic big data. Despite the obvious advantages, one of the inherent challenges is the need to specify the number of K clusters in advance.…”

“…The algorithm is also sensitive to the placement of the initial center point, which may lead to convergence to the local optimal solution. To alleviate this problem, it is recommended to use different center point initialization for multiple iterations [47].…”