计算机科学 ›› 2009, Vol. 36 ›› Issue (9): 196-200.

• 人工智能 • 上一篇    下一篇

基于高阶逻辑的复杂结构数据半监督聚类

李琳娜,陈海蕊,王映龙   

  1. (中国科学技术信息研究所 北京 100038);濮阳职业技术学院 濮阳 457000); (江西农业大学软件学院 南昌 330045)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然科学基金(60875029)资助。

Semi-supervised Clustering of Complex Structured Data Based on Higher-order Logic

LI Lin-na,CHEN Hai-rui,WANG Ying-long   

  • Online:2018-11-16 Published:2018-11-16

摘要: 半监督聚类近年来成为了机器学习和数据挖掘领域的研究热点。目前存在的半监督聚类方法都采用属性值的知识表示方式。但属性一值语言在表示复杂结构数据时存在很多弊端,而基于高阶逻辑的知识表示语言Escher 能较好地表示复杂结构数据。在Escher的知识表示方式下,首先当先验知识是实例之间的约束信息时,提出了搜索K-Means算法的K个初始质心的方法;其次,对先验知识不完全、能够发现的初始质心的个数r小于K的情况,提出了搜索其余的K-r个初始质心的算法MSS-KMeans和SMSS-KMEans;最后在复杂结构数据

关键词: 高阶逻辑,模板,半监督聚类,复杂结构数据,先验知识

Abstract: Semi supervised clustering algorithms have recently received a significant amount of attention in the machine learning and data mining of communities. All of current algorithms use attribute-value languages to represent knowledge. Attributcvaluc languages have inherent drawback for represent complex structured data. However, knowledge representation language Escher based on higher-order logic, can represent complex structured data. With Escher as knowledge representation formalism, firstly, when prior knowledge is pairwise constraints between instances, the method of initializing the K-Means algorithm was proposed; secondly, when the number r of cluster centers which can be initialized with incomplete knowledge is less than K, the algorithms MSS-KMeans and SMSS-KMeans were proposed to initialize the rest K一r cluster centers. Finally, the empirical study carried out on datasets of complex structure data showed the feasibility of the presented algorithms. I}he final experimental results demonstrate the comparability between the presented algorithms and the known algorithms based on attribute-value language.

Key words: Higher-order logic, Template, Semi-supervised clustering, Complex structure data, Prior knowledge

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