知识诱导的多个内核模糊集群
概括
本研究介绍了知识诱导的多个内核模糊集群 (KMKFC) 算法,通过域知识增强模糊集群. 这种新的方法改善了知识提取和数据映射,以获得卓越的集群性能.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 模糊集群方法从领域知识中受益,导致基于知识和数据的方法.
- 现有的方法在知识提取和融合机制方面面临挑战.
- 需要改进的模糊集群算法,有效地整合域名知识.
研究的目的:
- 提出一种新的知识诱导多核模糊聚类 (KMKFC) 算法.
- 增强在模糊集群中的知识提取和融合.
- 通过多个内核和域知识集成,提高模糊集群的适应性和性能.
主要方法:
- 开发了基于相对密度的知识提取 (RDKE) 方法,用于提取高密度的知识点和初始化集群中心.
- 引入了多个内核机制,以改善数据映射到高维空间,并增强聚类适应性.
- 集成的提取知识点通过知识影响矩阵进入KMKFC算法,以指导代集群过程.
- 建议RDKE与自动知识获取 (RDKE-A) 和KMKFC-A用于自动化知识点生成.
- 证明了KMKFC和KMKFC-A算法的融合.
主要成果:
- 拟议的KMKFC和KMKFC-A算法与其他13个算法相比,表现出更高的性能.
- 用四个标准评价指数和趋同速度来评估性能.
- 该RDKE方法有效地提取相关的知识点,用于初始化集群中心.
- 多个内核机制提高了算法的发现数据差异的能力.
结论:
- KMKFC和KMKFC-A算法代表了基于知识的模糊集群的重大进步.
- 这些方法有效地解决了知识提取和融合方面的局限性.
- 拟议的算法提供了改进的集群精度和更快的融合率,通过实验研究验证.
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