深度聚类:一个全面的调查
概括
深度聚类 (DC) 通过学习深度神经网络 (DNN) 的数据表示来增强机器学习. 本调查根据数据源对DC方法进行了分类,为复杂的集群应用提供了洞察力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 集群分析对于机器学习和数据挖掘至关重要.
- 有效的数据表示是聚类算法的关键.
- 深度集群 (DC) 利用深度神经网络 (DNN) 进行改进的表示.
研究的目的:
- 提供对深度集群方法的全面调查.
- 根据各种数据来源对DC方法进行分类.
- 解决现有调查的局限性,专注于单一视图和网络架构.
主要方法:
- 根据数据源对直流方法的系统分类.
- 区分基于方法,先验知识和架构的方法.
- 将DC分类为传统的单视图,半监督,多视图和转移集群.
主要成果:
- 直流方法分为四种主要类型:传统的单视图直流,半监督直流,深度多视图集群 (MVC) 和深度转移集群.
- 该调查强调了考虑DC应用的数据源的重要性.
- 分析涵盖了方法,先验知识和不同类别的架构差异.
结论:
- 本调查提供了基于数据源的深度聚类的结构化概述.
- 它确定了该领域的关键挑战和未来研究方向.
- 该分类为理解和推进直流技术提供了一个框架.
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