Case Selection Strategy Based on K-Means Clustering

被引：1

作者：

Ayeldeen, Heba ^{[1
,2
]}

Hegazy, Osman ^{[2
]}

Hassanien, Aboul Ella ^{[1
,2
]}

机构：

[1] SRGE, Cairo, Egypt

[2] Cairo Univ, Fac Comp & Informat, Cairo, Egypt

来源：

INFORMATION SYSTEMS DESIGN AND INTELLIGENT APPLICATIONS, VOL 1 | 2015年 / 339卷

关键词：

Knowledge management; Semantic similarity; Case-based reasoning; K-means;

D O I：

10.1007/978-81-322-2250-7_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Knowledge acquisition is considered as an extraordinary issue concerning organizations and decision makers nowadays. Learning from previous failures and successes saves plenty of time in understanding the problems and visualizing data. Case-based Reasoning (CBR) as a process is one of the most used methods to solve the problem of knowledge capture and data understanding. In this paper we proposed an approach for clustering theses documents based on CBR combined with lexical similarity and k-means algorithm for cluster-dependent keyword weighting. The cluster dependent keyword weighting help in partitioning and categorizing the theses documents into more meaningful categories. The proposed approach yield to 91.95 % increase of using CBR in comparison to human assessments.

引用

页码：385 / 394

页数：10

共 15 条

[1] Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies [J].