.png){kind=icon}
返回
SCEOMOO: A novel Subspace Clustering approach using Evolutionary algorithm, Off-spring generation and Multi-Objective Optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2023.110185.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Subspace clustering is an extension of conventional clustering techniques that seeks to find clusters in different subspaces within a data set with higher dimensions. But, recent research results indicate that there are few challenges to be addressed efficiently, such as finding number of subspace clusters automatically and getting optimal subspaces by satisfying multiple objective criteria. This paper focused on these two challenges and proposed a framework called Subspace Clustering using Evolutionary algorithm, Off-Spring generation and Multi-Objective Optimization (SCEOMOO) to find the optimal subspace clusters. The proposed SCEOMOO model is tested on six standard data sets with different validity indices and results are compared with well known existing subspace clustering methods. The comparative performance analysis reveals that the proposed SCEOMOO gives significant improvements in the performance with respect to some of the well known existing models.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
subspace clustering
evolutionary algorithm
multi-objective optimization
off-spring generation
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System物联网系统中基于AI的数据分析的新子空间聚类策略
PREAI
分享收藏Improved subspace clustering algorithm using multi-objective framework and subspace optimization
PREAI
分享收藏A common framework for understanding the origin of genetic dominance and evolutionary fates of gene duplications.png)
TRENDS IN GENETICS
TRENDS IN GENETICSIF16.3
PREAI
分享收藏Selecting Hyper-Parameters of Gaussian Process Regression Based on Non-Inertial Particle Swarm Optimization in Internet of ThingsIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏
查看更多内容