基于粗糙集的K均值聚类算法在案例检索中的应用

计算机科学 ›› 2010, Vol. 37 ›› Issue (12): 161-164.

基于粗糙集的K均值聚类算法在案例检索中的应用

陈千,向阳,郭鑫,王栋

(同济大学电子与信息工程学院计算机科学与技术系上海201804)

出版日期:2018-12-01 发布日期:2018-12-01
基金资助:
本文受国家自然科学基金项目(70371054,70771077),国家高技术863研究发展计划(2008AA04Z106)，上海市科委制造业信息化专项基金(08DZ1122303}资助。

Rough-set-based K-means Clustering Algorithm in Case Retrieval

CHEN Qian,XIANG Yang,GUO Xin,WANG Dong

Online:2018-12-01 Published:2018-12-01

摘要/Abstract

摘要： 在基于本体的案例检索系统中，由于数据库中的案例数量随着时间的推移而成倍增加，案例检索的效率不断降低，因此如何有效地提高案例检索系统的效率是个亚待解决的问题。提出一种基于粗糙集的k-means聚类算法，在用户检索之前对案例库中成千上万的案例进行有效聚类，从中定义基于粗糙集的聚类中心和上下近似以及边界。实验证明，该方法在系统检索时不必对每个案例都进行相似度的计算，从而大大提高了检索性能。

关键词: 粗糙集，K均值聚类，本体，案例检索

Abstract: The retrieval efficiency will fall down due to the increasing numbers of cases in Database in marketing Ontology hased case retrieval system. How to effectively improve the efficiency of case retrieval system is a serious prohlem. A K-means clustering algorithm based on rough set was proposed which can do the clustering work on thousands of marketing cases in Case Retrieval System in order to figure out the center of each cluster. So there is no need to do the similarity work in terms of each case. It is improved through experiment that the methods can largely enhance the performance of case retrieval system.

Key words: Rough set, K-means clustering, Ontology, Case retrieval

陈千,向阳,郭鑫,王栋. 基于粗糙集的K均值聚类算法在案例检索中的应用[J]. 计算机科学, 2010, 37(12): 161-164. https://doi.org/

CHEN Qian,XIANG Yang,GUO Xin,WANG Dong. Rough-set-based K-means Clustering Algorithm in Case Retrieval[J]. Computer Science, 2010, 37(12): 161-164. https://doi.org/

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