A Resource Search Model Using k-Means Clustering Analysis

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A Resource Search Model Using k-Means Clustering Analysis

更新时间：2025-07-09

- A Resource Search Model Using k-Means Clustering Analysis
- Vol. 46, Issue 10, Pages: 55-59(2012)
- 作者机构：
  
  西安建筑科技大学管理学院,西安,710055
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP311.52
- Online First：10 October 2012，
  
  Published：2012
- 稿件说明：
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A Resource Search Model Using k-Means Clustering Analysis[J]. 2012, 46(10): 55-59.
DOI：

A Resource Search Model Using k-Means Clustering Analysis[J]. 2012, 46(10): 55-59. DOI：

摘要

针对当前海量信息存储对等网络系统中资源搜索技术效率较低的问题

提出了一种采用k-均值聚类分析的高效搜索模型.该模型利用资源描述框架(RDF)描述的元数据进行聚类分析

使得资源的搜索由全局变为局部

从而有效地提高了资源搜索效率; 采用动态优化排序技术显著提高了查询的速度.通过子网分裂算法和节点备用算法增强了模型的可扩展性、安全性和可靠性.仿真结果表明

所提模型在查找时延和平均路径方面均比传统搜索模型更加高效、便捷.

Abstract

An efficient search model using k-means clustering analysis is proposed to improve the low efficiency of resource retrieval technology in peer-to-peer net with mass information. The metadata described by RDF framework is used to perform cluster analysis of resources and the search range of resources is narrowed from global to local so that the model can enhance the efficiency of resource retrieval effectively

a dynamic optimization technique is adopted to significantly improve the inquiry speed. Moreover

the use of the subnet division algorithm and the node backup algorithm enhances the scalability

safety and reliability of the model. Simulation results and comparisons with traditional retrieval models show that the proposed model is convenient and has higher resources searching efficiency in search delay and average path.

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Keywords

references

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