聚类分析
计算机科学
数据流聚类
数据挖掘
高维数据聚类
数据流挖掘
数据流
主成分分析
相关聚类
模糊聚类
降维
滑动窗口协议
CURE数据聚类算法
树冠聚类算法
模式识别(心理学)
窗口(计算)
人工智能
电信
操作系统
作者
Guohui Ding,Yankai Wang,Chenyng Li,Haohan Sun,Cailong Li,Lei Wang,Huiming Yin,Tiantian Huang
标识
DOI:10.1016/j.future.2023.04.008
摘要
At present, few studies have considered the clustering analysis of high-dimensional data collected by many sensors in a real-time streaming environment. Existing clustering analysis algorithms for high-dimensional data are primarily based on batch processing models, and most of them cannot meet the requirements of incremental high-dimensional data streams, which are extremely common in practical applications. To address the aforementioned problems, this paper focuses on the study of high-dimensional data clustering based on stream processing, and proposes a high-dimensional data stream clustering algorithm based on a feedback control system, which comprises three stages: window principal component analysis, feedback stream clustering, and feedback controller. The classic exponentially weighted attenuation function is used in the window principal component analysis to avoid concept drift in the data stream, and incremental feature extraction executes through the sliding window to improve the iterative efficiency of the data in the window. To minimize the errors caused by variability in projection angles during dimensionality reduction, a feedback stream clustering stage is designed with alternating iterations of window clustering and cluster aggregation. Aiming at the problems caused by manually adjusting the hyperparameters used in high-dimensional data stream clustering, a feedback controller is developed to adjust the hyperparameters in the two other stages by analyzing the clustering results in real time and using a discriminant score to adopt corresponding feedback strategies. The experimental comparisons between the proposed and the traditional algorithms on multiple datasets demonstrate the effectiveness of the former.
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