Functional Data Clustering Method Based on Shape Information and Functional Mahalanobis Distance

Abstract

Function-type data can provide insights into the internal structure of the data and facilitate the extraction of data features from the perspective of interactive derivative functions. The paper proposes a nonparametric clustering method for function-based data that incorporates first-order and second-order derivative function information into the Marginal distance for clustering function-based data. The method is based on the traditional K-means algorithm and is designed to cluster function-based data that contains rich shape information. The paper evaluated the performance of the algorithm of the text by comparing its purity and adjusted Rand coefficients against six other clustering algorithms on three different datasets. The results show that the algorithm of the text outperforms the other algorithms, demonstrating its outstanding performance, wide applicability, and practical significance in solving real-world problems.