基于相似度的信息颗粒更新方法  

Information Granules Updating Method Based on Similarity

作　　者：聂规划[1] 于珊珊[1] 刘平峰[1] 游怀杰[1] 

机构地区：[1]武汉理工大学电子商务与智能服务研究中心,湖北武汉430070

出　　处：《武汉理工大学学报（信息与管理工程版）》2015年第3期304-307,共4页Journal of Wuhan University of Technology：Information & Management Engineering

基　　金：教育部人文社科基金资助项目(10YJAZH050)

摘　　要：针对现有知识粒度研究中大多研究信息聚类而忽略增量更新的问题,提出了一种基于相似度的信息颗粒更新方法。通过计算新文本特征权向量与信息颗粒质心的相似度,将新文本添加到相似度最高且高于阈值的信息颗粒下,然后更新信息颗粒的质心,计算颗粒间的相似度,根据结果进行泛化、剪枝、细化和拆分等操作,完成信息颗粒的更新。将其与层次聚类法进行对比实验,结果表明,信息颗粒更新方法在保证性能的同时提升了效率。The existed studies of knowledge granularity mostly focus on information clustering while ignore studying incremental updating. In order to solve this problem,this paper proposed an information granules updating method based on similarity.This method added a new text to an existed information granule with the highest similarity which is higher than the threshold value by comparing similarities between features＇ weight vectors of the new text and the centroids of information granules. Then the updating of information granules was accomplished by relocating their centroids. This step included generalization,elimination,splitting and refinement based on the similarities between the granules. The comparative experiment with level clustering demonstrates that the new method can guarantee the performance as well as improve the efficiency.

关 键 词：信息颗粒 相似度 质心 增量更新 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]