Year: 2024
Type: conference-paper
Abstract: Commonly used entropy-based clustering algorithms, like the relative entropy fuzzy C-means clustering algorithm (REFCM), overlook the pivotal role of the centroid in iterations. Significantly, determining the precise centroid becomes challenging for REFCM in scenes characterized by complex structures or a substantial increase in the number of noise points. Therefore, this paper proposes an improved REFCM algorithm based on the gradient ascent algorithm (S-IREFCM_IF). Its advantages are as follows: (1) In order to obtain better initial results, the isolation forest algorithm is used to process the data. (2) Based on the Gaussian function, a gradient ascending iterative centroid algorithm with adaptive bandwidth parameters is proposed to determine a more accurate centroid and adapt to more data structures. (3) The suppressed operation is added to obtain a shorter algorithm running time. Experiments on synthetic datasets, and UCI datasets show that S-IREFCM_IF can obtain a more accurate centroid and better clustering performance.
Authors: Tangbao Zou, Jing Li, Gezi Shi
Institution: Xi’an University of Posts and Telecommunications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G3596670186, G515165631, 圆形直方图阈值分割法研究
OpenAlex ID: W4411730009